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WO2009039284A1 - Systèmes et procédés pour une détection et un classement à haut débit - Google Patents

Systèmes et procédés pour une détection et un classement à haut débit Download PDF

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Publication number
WO2009039284A1
WO2009039284A1 PCT/US2008/076869 US2008076869W WO2009039284A1 WO 2009039284 A1 WO2009039284 A1 WO 2009039284A1 US 2008076869 W US2008076869 W US 2008076869W WO 2009039284 A1 WO2009039284 A1 WO 2009039284A1
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Prior art keywords
detection
present
loading
detection environment
animals
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English (en)
Inventor
Hang Lu
Matthew Crane
Kwanghun Chung
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Georgia Tech Research Institute
Georgia Tech Research Corp
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Georgia Tech Research Institute
Georgia Tech Research Corp
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G01N33/5008—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
    • G01N33/5082—Supracellular entities, e.g. tissue, organisms
    • G01N33/5085—Supracellular entities, e.g. tissue, organisms of invertebrates
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/43504—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans from invertebrates
    • G01N2333/43526—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans from invertebrates from worms
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/43504—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans from invertebrates
    • G01N2333/43526—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans from invertebrates from worms
    • G01N2333/4353—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans from invertebrates from worms from nematodes
    • G01N2333/43534—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans from invertebrates from worms from nematodes from Caenorhabditis

Definitions

  • the various embodiments of the present disclosure relate generally to devices, systems, and methods of high-throughput detection and sorting. More particularly, the various embodiments of the present invention are directed to microfluidic systems and methods of high-resolution imaging and high-throughput sorting.
  • Small multicellular organisms such as Caenorhabditis elegans, Danio rerio, or Drosophila melanogaster are often used as model systems for providing new insights into genetics, developmental biology, disease, and drug discovery.
  • human gene homologs have been identified in these model organisms. Mutations in these homologs often result in observable phenotypic changes in the model organism and provide new biological insights into understanding disease states, cell lineage, neurobiology, cell death, and cancer, among others.
  • the modeling of diseases in multicellular organisms involves the generation of morphological or behavioral mutants with observable phenotypes. In many cases, these morphological or behavioral mutants are created to replicate human disease states.
  • researchers observe these model organisms and their interaction with candidate therapeutics for in vivo screens of libraries of pharmacological compounds. researchers can utilize these organisms and mutants thereof to identify novel compounds and cellular and molecular targets for drug intervention.
  • C. elegans The soil nematode, C. elegans, has become a particularly important multicellular organism for this type of research.
  • C. elegans is a small roundworm that has a generation time of about three days, which permits the rapid accumulation of large quantities of individual worms.
  • C. elegans has unique advantages.
  • C. elegans is extremely amenable to genetic approaches because its genome, anatomy, development, and behavior have been extensively studied, and a large collection of mutants have been isolated that are defective in embryonic development, behavior, morphology, and neurobiology, among others. Many knockout mutants are available for about 19,000 predicted genes, and the discovery and availability of RNA interference (“RNAi”) gene knockdowns has facilitated a broad range of genetic studies.
  • RNA interference RNA interference
  • C. elegans provides a unique opportunity to study and define neuronal mechanisms. Its transparent body and nearly invariant cell lineage coupled with fluorescent protein technology, enable precise cell-by-cell analysis of biological phenomena throughout the development of C. elegans. For example, the cell-lineage of C. elegans is fixed, allowing identification of each cell, which has the same position and developmental potential in each individual animal (e.g., muscle, gut, neuron, etc.).
  • C. elegans Although there are important physiological differences between nematodes and mammals, the conservation of many genes and fundamental cellular processes between nematodes and mammals make C. elegans an attractive organism for use in drug screening studies. Many biotechnology companies, as well as pharmaceutical companies, now employ C. elegans in their drug discovery processes. The above unique advantages of C. elegans combined with high-throughput genetic tools make C. elegans readily adaptable for automation and high-throughput experiments, such as pharmaceutical compound screens useful in the identification and development of potential candidate drugs.
  • the various embodiments of the present disclosure relate generally to devices, systems, and methods of high-throughput detection and sorting. More particularly, the various embodiments of the present invention are directed to microfluidic devices, systems, and methods of high-resolution imaging and high-throughput sorting.
  • an aspect of the present invention comprises a system, comprising a device for individually detecting and sorting a plurality of multicellular organisms having at least one phenotype at cellular resolution.
  • the device can be a single pass device or a multipass device.
  • the system further comprises an immobilization system.
  • the system can be configured for the individual detection and sorting a plurality of multicellular organisms, wherein the multicellular organism is Caenorhabditis elegans.
  • the system can further comprise a cooling system.
  • An aspect of the present invention comprises a detection system, comprising: a detection environment comprising at least one inlet and at least one outlet; a loading element, wherein the loading element in fluid communication with at least one inlet of the detection environment, the loading element adapted to a load one sample object into the detection environment; an immobilization system, wherein the immobilization system is in operational communication with the detection environment; and a detector, wherein the detector can detect a phenotype of a sample object located in the detection environment.
  • the detection system cam further comprise a container comprising a fluid and a plurality of sample objects having at least one phenotype, wherein the container is in fluid communication with at least one inlet of the at least one inlet of the detection environment.
  • the detection system can further comprise at least one unloading element, wherein the unloading element is in fluid communication with the at least one outlet of the detection chamber, wherein the unloading element in operational communication with a detector.
  • the detection system can further comprise a control system, wherein the control system receives a signal from the detector and controls the loading element, the unloading element, and the immobilization system.
  • the immobilization system can comprise a cooling system or at least one restraining element.
  • the sample object can comprise a unicellular or multicellular object.
  • the sample object can comprise Caenorhabditis elegans.
  • the system can comprise at least one restraining element, wherein the at least one restraining element is pressure-based restraining element, such as a valve or a suction element.
  • An aspect of the present invention comprises a microfluidic device, the device comprising: a detection environment comprising at least one inlet and at least one outlet; a loading element, wherein the loading element is in fluid communication with at least one inlet of the at least one inlet of the detection chamber, the loading element adapted to a load a sample object into the detection chamber; at least one immobilization element, wherein the at least one immobilization element is in operational communication with the detection environment; an unloading element, wherein the unloading element is in fluid communication with the at least one outlet of the detection environment.
  • at least one of the at least one immobilization element comprises a cooling element or at least one restraining element.
  • the sample object can comprise a unicellular or multicellular object.
  • the multicellular object can comprise Caenorhabditis elegans.
  • the device can comprise at least one restraining element, wherein the at least one restraining element is pressure-based restraining element, such as a valve or a suction element.
  • An aspect of the present invention comprises a method for detecting at least one phenotype of an object, the method comprising: loading a microfluid comprising a single object from a fluid comprising a plurality of objects into a microfluidic device; immobilizing the object; and detecting the phenotype of the object.
  • immobilizing the object can comprise restraining the object or cooling the microfluid.
  • the method can further comprise unloading the object from the microfluidic device.
  • the method can further comprise repeating the loading, the immobilizing, the detecting, and the releasing at least once.
  • unloading the object from the microfluidic device can comprise sorting the object.
  • the object can comprise a unicellular or multicellular object.
  • the multicellular object can comprise Caenorhabditis elegans.
  • the method is automated.
  • Figure 1 is a schematic of the microsystem functioning in rapid imaging, phenotyping, and sorting of a mixed population of animals based on cellular and subcellular phenotypes.
  • Figures 2A-B are optical micrographs of the central region of the microfluidic device showing device components.
  • Figure 3A is a schematic illustration the process of fabricating a PDMS microfluidic device.
  • Figure 3B is an exploded view of a schematic illustration showing the individual lithography layers for a PDMS microfluidic device.
  • Figure 3C is a schematic of a cross- sectional view of a valve.
  • Figure 4 illustrates the pressure profile in the detection environment.
  • Figures 5A-D are schematic diagrams demonstrating the valve control sequence in the worm sorting process.
  • Figures 5E-G are micrographs showing automated imaging and sorting sequence.
  • Figure 6 is a diagram showing the on-chip and off-chip components and features of the microfluidic system.
  • Figure 7 is a schematic of a microfluidic device having two detection environments.
  • Figure 8A is a schematic of a fluorescent Caenorhabditis elegans expressing AQR and PQR.
  • Figures 8B-M show automated gene expression pattern analysis in the microchip.
  • Figure 8N is a graph of the percentage of animals with each of the possible expression patterns in > 1,000 animals.
  • Figure 8O is a histogram of animal loading time into the detection environment.
  • Figures 9A-D is a schematic representation of automated image processing and a decision-making process to sort animals at a cellular resolution.
  • Figures 10A-H illustrate automated high-throughput imaging and sorting based on synaptic phenotypes.
  • Figures 101 -J compares the images of a worm imaged in the presence and absence of cooling.
  • Figure 1OK demonstrates the puncta structure of the nerve cord in a mutant animal expressing punc-25-YFP::RAB-5 before significant photobleaching (top) and quantification of puncta fluorescence from line scans with different amounts of photobleaching (bottom).
  • Figures HA-B show a computer-control, computer-enhanced image processing for a fast screen of C. elegans.
  • Figures 12A-L demonstrates computer-assisted phenotyping to identify mutants of interest.
  • Multicellular organisms such as C. elegans and D. melanogaster
  • C. elegans and D. melanogaster are important genetic models for studying developmental biology, physiology, and disease.
  • fully sequenced genomes and techniques that interrogate the functions of genes on large scales such as protein microarrays, nucleic acid microarrays, and RNAi knockdowns, have become prevalent and important in these model organisms because of the high- throughput nature of these methods.
  • important techniques for phenotyping, such as microscopy are still largely limited in their manual modes of operation, making them not only low in throughput, but also prone to human biases and errors.
  • in vivo microscopy can be used for characterizing morphology and gene expression patterns of cells and tissues and for visualizing expression patterns, localization, synthesis, and degradation of molecules.
  • This type of manual microscopy screen usually takes many months to perform, is very labor-intensive, and the phenotypes are usually qualitative. This creates a bottleneck for performing genetic analysis as phenotyping limits the speed of discovering new biological mechanisms and pharmacological compounds..
  • Microfluidics lends itself to solving some of these problems.
  • the term "microfluidic" and derivatives thereof refer to systems and methods of manipulation of small amounts of fluids (about 10 ⁇ 9 to about 10 ⁇ 18 liters) using channels with dimensions of a few to thousands of micrometers (about 1 ⁇ m to about 2000 ⁇ m).
  • Microfluidic systems have distinctive physical characteristics compared to macroscopic systems. In a microchannel, when two fluid streams come together, the flow is laminar (usually at very low Reynolds number, for example, less than about 10) and the dominant mixing mechanism is the result of diffusion of molecules across the interface between the fluids.
  • microfluidic systems This unique behavior of liquids at the microscale allows for greater control of the concentration of chemicals, culturing environment of cells, and even multicellular organisms.
  • a large surface-area-to-volume ratio and small thermal mass facilitate rapid heat transfer in microfluidic systems and enable precise spatial-temporal temperature control.
  • many electrical components can be integrated on a chip having a microfluidic device and allow the microsystems to perform complex functions.
  • the scale of microfluidic systems matches that of small organisms, cells, and macromolecules.
  • fluid is used herein for convenience and refers generally to many fluids, liquids, gases, solutions, suspensions, gels, dispersions, emulsions, vapors, flowable materials, multiphase materials, or combinations thereof.
  • a fluid can comprise a mixture of a plurality of fluids.
  • plural refers to more than one.
  • a fluid is a culture medium, a biologically buffered solution, a salt solution, or the like.
  • Various embodiments of the present invention are directed to automated, microfluidic systems and methods of high-throughput detection of cells and multicellular organisms.
  • Some embodiments of the present invention permit high-throughput detection of a plurality of multicellular organisms through a single-pass through the system of the present invention. Some embodiments of the present invention are directed to automated, microfluidic systems and methods of high-throughput detection and sorting of cells and multicellular organisms. Some embodiments of the present invention permit the high throughput analysis and high-resolution 3D imaging of multicellular organisms.
  • An embodiment of an automated microfluidic system comprises image processing capabilities for performing high -resolution, high-throughput imaging, phenotyping, and sorting of cells and multicellular organisms.
  • image processing capabilities for performing high -resolution, high-throughput imaging, phenotyping, and sorting of cells and multicellular organisms.
  • microdevices From single-cell assays to cell sorting, many microdevices have been developed, which have revolutionized the throughput of experiments in the area of single cell studies. The impact of such microdevices, however, has not yet been realized for multicellular organisms, which is attributable in part to the difficulties of handling live, moving multicellular organisms. To date, manual microscopy is the only way to obtain 3D images of multicellular organisms at subcellular resolution.
  • Microscopy applications are currently limited as they can be performed on only a limited number of animals, and the results obtained can be strongly affected by stochastic variations among individuals.
  • Embodiments of the system and methods of the present invention can automate sample handling and image analysis, reducing experimental time and human intervention. This greatly improves throughput and data quality of experimentation by increasing the number of individuals that can be examined and decreasing the effect of environmental noise.
  • Some embodiments of the present invention contemplate a microsystem that can automatically process a population of cells or multicellular organisms by imaging the cells or multicellular organisms one at a time at cellular or subcellular optical resolution in two- dimensions or three-dimensions, processing the images, determining the phenotype of the cells or multicellular organisms, and sorting the cells or multicellular organisms according to the identified phenotype, without human intervention ( Figure 1).
  • advantages may include but are not limited to: (1) automation; (2) resolution at single-cell and/or subcellular levels; (3) a throughput that is at least an order of magnitude faster than that by manual operation; (4) reduction of human bias and errors (5) reduction in photobleaching; (6) compatibility with many microscope and camera systems; (7) relative inexpensive nature of the setup; (8) applicability and scalability to many model organisms and cell types; (9) as self-regulated single organism loading scheme; (10) reduction in the use of anesthetics; and (11) on-line quantitative analysis for screens, sorting, and gene expression profiling
  • An aspect of the present invention comprises a microfluidic device 100, the device comprising: a detection environment 105 comprising at least one inlet 110 and at least one outlet 115; a loading element 130 located upstream from the detection environment 105, wherein the loading element 130 is in fluid communication with at least one inlet 110 of the detection environment 105, the loading element 130 adapted to a load a sample object into the detection environment 105; at least one immobilization element, wherein the at least one immobilization element is in operational communication with the detection environment 105; and a unloading element 140 located downstream from the detection environment 105, wherein the unloading element 140 is in fluid communication with the detection environment 105 and the at least one outlet 115 of the detection environment 105.
  • an exemplary embodiment of the present invention comprises a microfluidic device 100, the device comprising: a detection environment 105 comprising at least one inlet 110 at least one outlet 115, and at least one restraining element 125; a loading element 130 located upstream from the detection environment 105, wherein the loading element 130 is in fluid communication with at least one inlet 110 of the detection environment 105, the loading element 130 adapted to a load a sample object into the detection environment 105; a cooling element 135, wherein the cooling element is in thermal communication with the detection environment 105; and a unloading element 140 located downstream from the detection environment 105, wherein the unloading element 140 is in fluid communication with the detection environment 105 and the at least one outlet 115 of the detection environment 105.
  • a number of different cell types and multicellular organisms may be employed as sample objects in the systems and methods of the present invention.
  • the systems and methods of the present invention are applicable to many cell types (e.g., mammalian cells, tissue culture cells, among others) and multicellular organisms know in the art, including animals, such as insects, amphibians, and fish, plants, fungi, seeds, and the like.
  • Specific organisms of interest include, but are not limited to the genera of Xenopus, Danio, Caenorhabditis, Drosophila, and the like.
  • the embryos of many animals and plants can be employed in the systems and methods of the present invention.
  • the multicellular organisms employed in the present invention may be at many stages of their life (e.g., in the larval stage, the adult stage, etc.). Throughout the present application, specific reference may be made to analysis of specific organisms or cells; however, such references are not intended to limit the scope of the invention as the systems and methods of the present invention are suitable and can be configured for the analysis of many types of cells and multicellular organisms.
  • the a micro fluidic device 100 can be made of many materials, including but not limited to poly(dimethylsiloxane) (PDMS), polyurethanes, polyimides, polysilanes, polysiloxanes, polysilazanes, and other elastomers known in the art.
  • a microfluidic device 100 is made of PDMS.
  • PDMS is optically transparent, so it can be used with conventional optical methods of detection.
  • its biocompatibility makes this elastomer particularly suitable for work with living cells and organisms.
  • PDMS is a uniquely suitable material for handling living organisms because of its low toxicity and high permeability to oxygen and carbon dioxide.
  • the microfluidic device 100 comprises at least two layers, the at least two layers comprising a worm loading layer and a valve control layer.
  • the microfluidic device 100 comprises at least three layers, the at least three layers comprising a worm loading layer, a valve control layer, and a membrane layer.
  • the channels containing the cooling fluid can be integrated into the worm flow layer, the valve control layer, a separate cooling layer, or combinations thereof.
  • two different molds can be fabricated by photolithographic processes to create a worm loading layer and a control layer.
  • the mold for the worm loading layer can be made by a two-step photolithographic process.
  • a negative photoresist of about 1-100 ⁇ m can be spin-coated onto a substrate (e.g., silicon wafer) for the worm loading chamber and the detection environment.
  • the loading element, side channels (i.e., restraining element), inlet, and outlet can then be fabricated with a layer of positive photoresist of about 10-100 ⁇ m on the same substrate.
  • the substrate can be heated at about 125 0 C for about 5 min to allow the positive photoresist to reflow so that the channels form a substantially smooth and substantially rounded shape.
  • the master for the control layer can be made of a layer of negative photoresist of about 10-100 ⁇ m on a substrate.
  • the two molds and a blank substrate can be treated with tridecafluoro-1,1,2,2- tetrahydrooctyl-1-trichlorosilane vapor or the like to prevent adhesion of PDMS during the molding process.
  • PDMS can be poured onto the control-layer master to obtain a layer of about 5 mm in thickness.
  • Mixture of PDMS and tetrahydrofuran (THF) in a 2:1 ratio can be spin-coated on a substrate to produce a thin layer having a thickness of about 20 ⁇ m to form a membrane. Both can be partially cured at about 70 0 C for about 20 min.
  • the thick control layer can then be peeled off from the master and holes can be punched for access to the control and cooling channels.
  • the control layer can then be bonded to the thin PDMS membrane on the substrate. This assembled control layer can be cured at about 70 0 C for about 2 hours.
  • PDMS PDMS was spin-coated onto the master to give a layer having a thickness of about 60 ⁇ m.
  • the worm-loading layer can be cured at about 70 0 C for about 2 hours and then can be peeled off from the master.
  • the layer can be then turned up side down and bonded to the control layer using an oxygen plasma treatment or the like. Another set of holes can then be punched for access to the worm loading channel.
  • These assembled layers can then bonded onto a suitable substrate, such as a glass, to form the microdevice.
  • PDMS is a soft material with Young's modulus of approximately 750 kPa, which can be deflected with small forces. Structures with a high aspect ratio, such as the worm loading chamber, are especially prone to deformation and storing energy when pressure is applied. Once the pressure is removed, the deformed PDMS slowly returns to the former state and releases the stored energy. This mechanical compliance of the device causes flow fluctuation in the detection environment and thereby disturbs a loaded worm. To reduce flow fluctuation in the detection environment, on-chip valves (e.g., loading elements, at least one restraining element, and unloading elements) using multilayer soft lithography were fabricated.
  • on-chip valves e.g., loading elements, at least one restraining element, and unloading elements
  • Embodiments of the microfluidic device 100 of the present invention can comprise a detection environment 105 comprising at least one inlet 110, at least one outlet 115, and at least one restraining element 125.
  • a detection environment 105 can comprise one inlet 110.
  • a detection environment 105 can comprise two, three, four, or more inlets 110.
  • a detection environment 105 can comprise one outlet 115.
  • a detection environment 105 can comprise two outlets 115.
  • a detection environment 105 can comprise three, four or more outlets 115.
  • the number of outlets from the detection environment is likely directly related to the number of detectable characteristics or phenotypes desired to be sorted.
  • a receptacle can be associated with the at least one outlet to capture sorted objects.
  • the detection environment 105 comprises the portion of the microfluidic device
  • the detection environment 105 comprises a channel designed to accommodate a sample object.
  • the features of the lithography masters determine the features and parameters of the microfluidic device.
  • the features and parameters of the microfluidic device can vary depending upon the specific application and the sample objects (e.g., cells or multicellular organisms) of interest.
  • the parameters of the channels can be customized to accommodate the shape and size of the sample. For example, in the case of C.
  • the detection environment can comprise a substantially longitudinal channel having a diameter on about the same order as the width of a worm (e.g., about 25-30 ⁇ m for an L4 worm) to physically constrain the worm in the channel and restrict its mobility.
  • the substantially longitudinal channel can have a diameter of about 5 ⁇ m to about 60 ⁇ m.
  • the substantially longitudinal channel can have a diameter of about 15 ⁇ m to about 20 ⁇ m.
  • Various embodiments of the present invention comprise a microfluidic device 100 comprising at least one immobilizing element.
  • at least one of the at least one immobilizing element comprises at least one pressure-based restraining element 125.
  • At least one of the at least one immobilizing element comprises a cooling element 135.
  • the at least one restraining element 125 functions to physically restrain the sample in the detection environment and minimize movement of the sample (e.g., C. elegans).
  • the at least one restraining element 125 comprises a pressure-based restraining element.
  • the at least one restraining element 125 can comprise a suction element.
  • the at least one restraining element 125 can comprise a plurality of suction elements.
  • a plurality of suction elements can comprise a series of parallel channels forming a pillar array (also referred to as "side channels") with each channel separated by about 20 ⁇ m to about 50 ⁇ m.
  • the at least one restraining element can comprise a valve ( Figures 2B and 3C).
  • a "valve" is a device that regulates the flow of fluids and sample objects within fluids by opening, closing, or partially obstructing various passageways.
  • the microfluidic device 100 comprises a cooling element 135, wherein the cooling element is in thermal communication with the detection environment 105.
  • the cooling element 135 is capable of locally cooling the detection environment 105.
  • Cooling the detection environment 105 thermally reduces the mobility of the sample and permits imaging at cellular and subcellular resolutions.
  • the detection environment 105 is cooled to about 4 0 C.
  • the cooling element 135 comprises a channel in thermal communication with the detection environment 105. The use of a cooling element 135 eliminates the need to use anesthetics to immobilize worms, which often disrupt neuronal signaling, induce undesirable physiological changes, and may be toxic to the organisms of interest.
  • the microfluidic device 100 comprises a loading element 130 located upstream from the detection environment 105, wherein the loading element 130 is in fluid communication with the at least one inlet 110 and the detection environment 105, the loading element 130 adapted to a load a sample object into the detection environment 105.
  • the microfluidic device also comprises an unloading element 140 located downstream from the detection environment 105, wherein the unloading element 140 is in fluid communication with the detection environment 105 and the at least one outlet 115 of the detection environment 105.
  • the loading element is a valve
  • the unloading element is a valve.
  • the loading element 130 automatically self-regulates the loading of a single nematode into the detection environment 105 by the design of the loading scheme through operational communication with the unloading element 140.
  • Multiple worms in the detection environment 105 can cause many problems, including but not limited to: (1) significant distortion of the shape and orientation of a worm of interest, which can affect visualization of the native morphology of the sample microorganism and can cause errors in image processing; (2) mistaken identification of samples of interest as fluorescence signals from other worms in the field of view can be mistakenly identified as the worm of interest, causing significant errors in sorting and laser ablation; and (3) aggregation of worms causes clogging of the channel.
  • some embodiments of the present invention employ a self- regulated loading scheme. This scheme to load one worm at a time takes advantage of the squeezable body of the nematode and pressure drops created by a loaded worm ( Figure 4). C. elegans is enclosed by an elastic cuticle layer. This layer is pushed outward by a high internal hydrostatic pressure relative to the ambient environment, which results in the nematode adopting a cylindrical structure.
  • the elastic cuticle layer For a worm to pass into and through the detection environment that has a narrower channel width than the diameter of a worm, the elastic cuticle layer must be deformed against the hydrostatic pressure by the force generated by the pressure drop across the worm.
  • the entire pressure drop occurs over the single worm in the detection environment, and this is great enough to deform the worm and push it into the detection environment.
  • the pressure drop across a second worm in the channel upstream and outside the detection environment is too small to push the animal into the detection zone. Once a loaded worm leaves the detection environment, however, the pressure drop across the loading element then becomes large enough to push the second worm into the detection environment.
  • the at least one unloading element is closed while the side positioning channels remain open ( Figure 5A), and a constant pressure source is used to drive the flow of a fluid containing the sample object into the microfluidic device.
  • Self- regulation of loading plays a role in high- resolution imaging and accurate sorting.
  • the flow resistance is increased.
  • the reduced flow rate lowers the pressure on a second animal at the loading element located at the entrance of the detection environment to a point where it is not sufficient to push the second animal into the detection environment ( Figure 4).
  • the hydrodynamic resistance of the positioning channels self-equalizes.
  • the animal stops moving in the direction of the flow ( Figure 5B).
  • the channels of the pillar array can be opened to generate a pressure gradient to guide an animal into the detection environment. This distribution of the pressure force minimizes mechanical stress on the animal.
  • the animal is cooled, immobilized, and imaged (Figure 5C) before being phenotyped and sorted accordingly ( Figure 5D).
  • Figures 5E-G are optical micrograph showing automated imaging and sorting sequence: (E) loading nematode into the detection zone; (F) a loaded animal preventing a second animal from entering; and (G) the second animal is automatically moved into the detection zone after the previous animal exits the detection zone.
  • One advantage of the design of both the at least one restraining element and loading element in the main channel is that there are no permanent small features (i.e., ⁇ 20 ⁇ m).
  • the dimension of the at least one restraining element and loading element is not from the mask design but instead from the partially closed valves, and therefore is tunable and can be expanded if necessary. Because pieces of debris smaller than the size of the nematodes cannot be easily filtered out, this design feature prevents clogging of the channels as the valves can be opened to remove the debris when necessary.
  • An aspect of the present invention comprises a system for high-throughput detection of a characteristic of a sample object, the system 200 comprising a detection environment 105 comprising at least one inlet 110 and at least one outlet 115; a loading element 130 located upstream from the detection environment 105, wherein the loading element 130 is in fluid communication with the at least one inlet 110 and the detection environment 105, the loading element 130 adapted to a load a sample object into the detection environment 105; at least one immobilization element, wherein the at least one immobilization element is in operational communication with the detection environment 105; an unloading element 140 located downstream from the detection environment 105, wherein the unloading element 140 is in fluid communication with the detection environment 105 and the at least one outlet 115 of the detection environment 105; a container 205 comprising a fluid 210 and a plurality of sample objects 215, wherein the container 205 is in fluid communication with at least one inlet of the at least one inlet 115 of the detection environment 105; a drive system 220 that drives the
  • an exemplary embodiment of the present invention comprises a system for high-throughput detection of a characteristic of a sample object as illustrated in Figure 6, the system 200 comprising a detection environment 105 comprising at least one inlet 110 and at least one outlet 115; a loading element 130 located upstream from the detection environment 105, wherein the loading element 130 is in fluid communication with the at least one inlet 110 and the detection environment 105, the loading element 130 adapted to a load a sample object into the detection environment 105; a cooling element 135, wherein the cooling element is in thermal communication with the detection environment 105; a unloading element 140 located downstream from the detection environment 105, wherein the unloading element 140 is in fluid communication with the detection environment 105 and the at least one outlet 115 of the detection environment 105; a container 205 comprising a fluid 210 and a plurality of sample objects 215, wherein the container 205 is in fluid communication with at least one inlet of the at least one inlet 115 of the detection environment 105; a drive system
  • the system 200 can comprise a container 205 comprising a fluid 210 and a plurality of sample objects 215.
  • the container can be in fluid communication with the drive system 220 and at least one inlet of the at least one inlet 115 of the detection environment 105.
  • the container 205 can comprise many containers suitable for the culture or dispensation of sample objects 215.
  • the container 205 is adapted to withstand forces provided by the drive system 220.
  • the container can be a flask, a test tube, a microtube, a bottle, or the like. Some embodiments of the present invention may comprise more than one containers 205.
  • a single container 205 may be in fluid communication with an inlet 110 of the detection environment 105. In another embodiment of the present invention, more than one container 205 may be in fluid communication with an inlet 110 of the detection environment 105.
  • the container contains a fluid 210A.
  • the fluid comprises a medium or buffer solution that is compatible with the sample objects of interest. For instance, for detection and sorting of C. elegans, the fluid 210A may be M9 buffer solution.
  • Various embodiments of the present invention comprise a microfluidic system 200 comprising at least one immobilizing element.
  • at least one of the at least one immobilizing element comprises at least one pressure-based restraining element 125.
  • at least one of the at least one immobilizing element comprises a cooling element 135.
  • the at least one immobilizing element comprises at least one pressure -based restraining element 125 and a cooling element 135.
  • Embodiments of the present invention comprise a cooling system 225, wherein the cooling system 225 is in thermal communication with the detection environment 105 via the cooling element 135.
  • the cooling system comprises a cooling fluid 210B.
  • the cooling fluid can comprise many suitable cooling fluids, including but not limited to, a salt solution, or many coolants that has a low freezing point, such as a high salt solution or a glycerol solution, among others.
  • cooling is achieved by flowing a fluid 210B having a temperature of about 0 0 C to about -10 0 C through a cooling element 135 fabricated in the control layer of the device 150 and integrated beneath the detection environment where the sample object is restrained.
  • a fluid 210B having a temperature of about 0 0 C to about -10 0 C
  • a cooling element 135 fabricated in the control layer of the device 150 and integrated beneath the detection environment where the sample object is restrained.
  • the cooling fluid temperature varies depending on the thickness of the microfluidic device (e.g., thickness of the PDMS) and substrates supporting the microfluidic device (e.g. glass layer).
  • the fluid 210B can be flowed off-chip and chilled through small metal tubes adjacent to a Peltier cooler or a refrigerated fluid bath.
  • the cooling system e.g., Peltier cooler
  • the temperature of the fluid 210B can be precisely controlled.
  • Embodiments of the present system 200 also comprise a drive system 220 that drives the fluids 210A, 210B, and 210C of the system 200.
  • a drive system 220 can comprise a compressed gas cylinder in fluid communication with a plurality of gas regulators.
  • the drive system comprises a compressed gas cylinder of a fluid 210C.
  • the fluid 210C can comprise many fluids known in at art, including but not limited to N 2 or compressed air at a pressure of about 10 psi to about 100 psi.
  • the drive system may comprise a pump.
  • suitable pumps include but are not limited to a pulsatile pump (e.g., a peristaltic roller pump), a rotodynamic pump (e.g. centrifugal pump), a positive displacement pump (e.g., root-type pumps, reciprocating-type pumps, or compressed air-powered double-diaphragm pumps), a kinetic pump, or a gear pump, among others.
  • the drive system can comprise a compressed gas cylinder, a pump, or combinations thereof.
  • the system 200 can be configured to be driven by suction.
  • the drive system 220 provides a pressure by urging a fluid 210C into the container 205, which in turn urges the flow of a fluid 210A comprising a plurality of sample objects 215 into the at least one inlet 110 of the detection environment 105.
  • cooling is achieved by providing a pressure by the drive system 220 that urges a fluid 210C into the cooling system 225, which in turn urges the flow of a fluid 210B having a temperature of about 10 0 C to about 0 0 C to the cooling element 135 fabricated in the control layer of the device 100 and integrated beneath the detection environment 105 where the animal is restrained.
  • the drive system 220 provides a pressure by urging a fluid 210C into voids within the control layer.
  • the pressure created by the fluid 210C is sufficient to actuate the PDMS membrane causing the membrane to deflect creating a valve.
  • the drive system may comprise a plurality of control valves 240 to control specific actuation and the degree of actuation of the valves comprising the loading element 120, the at least one restraining element 125, and the unloading element 140.
  • Embodiments of the present system 200 also comprise a detector 230 capable of detecting a phenotype or characteristic of a sample object.
  • the detector 230 can be many detectors known in the art, including but not limited to, an optical detector, a radiation detector, a magnetism detector, or other detectors capable of recognizing phenotypes or characteristics of a sample object.
  • An optical detector can comprise a microscope, a lens system, a CCD device, a camera, a video recorder, or a photomultiplier tube, among others.
  • a microscope can be an optical microscope, including but not limited to, a transmitted light microscope (including operations in bright field mode, differential interference contrast mode, and phase contrast mode, among others), a fluorescent microscope, a confocal microscope, or a multiphoton microscope.
  • a sample object can be visulaized in two dimesions or three dimensions.
  • the systems and methods of the present invention permit detection of at least one phenotypic marker in a population of cells or multicellular organisms.
  • the systems and methods of the present invention can detect and sort cells or multicellular organisms expressing a fluorophore, for example a fluorescent molecule, dye, or protein, including but not limited to, green fluorescent protein (e.g., GFP, EGFP), red fluorescent protein (RFP), blue fluorescent protein (EBFP), cyan fluorescent protein (CFP) and yellow fluorescent protein (YFP), and derivatives thereof.
  • a fluorophore for example a fluorescent molecule, dye, or protein, including but not limited to, green fluorescent protein (e.g., GFP, EGFP), red fluorescent protein (RFP), blue fluorescent protein (EBFP), cyan fluorescent protein (CFP) and yellow fluorescent protein (YFP), and derivatives thereof.
  • Some embodiments of the systems and methods of the present invention allows for the detection and analysis of not only the intensity of fluorescence of an organism, but also the location of fluorescence within the multicellular organism at cellular and subcellular resolutions. Some embodiments of the systems and methods of the present invention permit the differentiation of multicellular organisms based on the expression (e.g., intensity), morphology, and/or localization of at least one fluorophore to sort and separate an organism having a first phenotype from an organism having a second phenotype, or a third phenotype, and so forth. In some embodiments of the present invention, the phenotypic trait can result from exposure of an organism to a compound, for example, a pharmaceutical compound.
  • a phenotype can result from genetically crossing two genotypically different multicellular organisms.
  • the automated system of the present invention can be used to separate multicellular organisms that are at a particular stage of development. Examples of applicable multicellular organisms are all stages developmental of C. elegans, D. melanogaster larvae and embryos, or Xenopus or D. rerio embryos.
  • the system 200 comprises a control system 235 which receives at least one signal from the detection element 120 and controls the loading element 110, at least one restraining element 125, and unloading element 140 via the drive system 220 in response to that signal.
  • the control system 235 comprises an image acquisition component, an image processing component, and an image recognition component, as well as components to automate pressure control, control of the detector (e.g., the stage of the microscope), and feedback control of the valves. It is a self- contained and closed-loop system that needs minimal human intervention.
  • the system and methods of the present invention are capable of a single pass detection and sorting of multicellular organisms with accuracy greater than about 50%.
  • the system is capable of performing a single pass phenotyping and sorting of multicellular organisms with accuracy greater than about 75%.
  • the system and methods of the present invention are capable of a single pass detection and sorting of multicellular organisms with accuracy greater than about 85%.
  • the system is capable of performing a single pass phenotyping and sorting of multicellular organisms with accuracy greater than about 90%.
  • the system and methods of the present invention are capable of a single pass detection and sorting of multicellular organisms with accuracy greater than about 95%.
  • the system is capable of performing a single pass phenotyping and sorting of multicellular organisms with accuracy greater than about 97%.
  • the system and methods of the present invention are capable of a single pass detection and sorting of multicellular organisms with accuracy greater than about 99%.
  • systems and methods are capable of recirculation of sample object.
  • systems and methods of the present invention are capable of multipass phenotyping and sorting of multicellular organisms.
  • the term “accuracy” indicates the ability of the system to correctly identify of a phenotype of interest.
  • the term “single pass” as used herein refers to the detection and sorting of a sample object without having to re-circulate a sample object.
  • the term “multipass” refers to the detection and sorting of a sample object wherein at least one sample object must be re-circulated.
  • a cycle of the system comprises loading a sample object into the detection environment, the animal is detected (e.g., by its auto-fluorescence) and the valves surrounding the chamber are closed.
  • the camera and stage of the microscope are then controlled to grab a series of images that cover the three-dimensional volume that the animal occupies; the images are then stored and, if sorting is desired, are processed in real time to determine the phenotype of the animal and select the proper exit channel by triggering the corresponding outlet valve to open.
  • the program waits until the animal leaves the observation chamber. The exit channel is then closed, and this completes a cycle.
  • the processing cycle starts over again.
  • This sequence of events can occur in less than about twenty seconds, or less than about fifteen seconds, or less than about ten second, or less than about five seconds. In an embodiment of the present invention, this sequence of events can occur in less than about one second. In an embodiment of the present invention, this sequence of events can occur in about 0.1 seconds.
  • the systems and methods of the present invention can be automated and may require minimal human intervention. Automation of system reduces the processing time of such experiments, reduces the incidence of photobleaching of fluorescent markers, and reduces or eliminates some of the biases introduced by manual handling.
  • the systems and methods of the present invention can be easily adapted for a wide variety of microscopy-based techniques, including but not limited to fluorescence recovery after photobleaching (FRAP), laser ablation of cells, and laser cutting of neuronal processes.
  • devices, systems and methods of the present invention may comprise one or more detection environments. ( Figure 7).
  • a microfluidic device 300 may comprise two detection environments 105.
  • systems and methods of the present invention may comprise a plurality of detection environments or an array of detection environments. Despite the plurality of detection environments, at least one loading element remains configured to load one sample object at a time into a detection environment.
  • Microfluidic device fabrication The microfluidic device was fabricated using multi-layer soft lithography. Two different molds were first fabricated by photolithographic processes to create worm loading layer and the control layer. The mold for the worm loading layer was made by a two-step photolithographic process. In the first step, a 30 ⁇ m thick negative photoresist (SU8-2025, Microchem) was spin-coated onto a silicon wafer for the worm loading chamber and the detection channel. The loading regulator, side channels, and outlets were then fabricated with a 25 ⁇ m layer of positive photoresist (AZ 50XT, AZ Electronic Materials) on the same wafer.
  • AZ 50XT positive photoresist
  • the master for the control layer was made of a 50 ⁇ m layer of negative photoresist (SU8-2050, Microchem) on a silicon wafer.
  • the two molds and a blank wafer were treated with tridecafluoro-lj ⁇ -tetrahydrooctyl-l-trichlorosilane vapor (United Chemical Technologies ) in a vacuum desiccator to prevent adhesion of PDMS during the molding process.
  • Polydimethylsiloxane (PDMS, Sylgard 184, Dow Corning A and B in 5:1 ratio) was poured onto the control-layer master to obtain a 5 mm- thick layer.
  • Strains used in this work include tax-4(ks28); kyls342 [pgcy-32::tax- 4::GFP, punc-122::GFP], kylsl4 ⁇ [str-2::GFP + lin-15(+)], kylsl4 ⁇ ; rol-6(el87); slo- I(ky399), julsl98 [punc-25-YFP::rab-5], and a mutant that also carries julsl98.
  • C. elegans culture and sample preparation Animals were cultured according to established methods. Synchronized L4 worms were prepared as follows: eggs were obtained by bleaching adults using a solution containing about 1% NaOCl and 0.1N KOH, washed and let hatch overnight in M9 buffer, and cultured on Nematode Growth Medium (NGM) plates seeded with E. coli OP50. Animals were washed and suspended in M9 solution containing 0.5 wt% Bovine Serum Albumin (BSA) for each experiment. Filtering device fabrication. To get rid of dust particles and debris in the worm suspension, a filtering device was fabricated using single-mold soft lithography.
  • the mold was fabricated to obtain 40 ⁇ m thick structures using SU8-2025 on a silicon wafer, and was treated with tridecafluoro-lj ⁇ -tetrahydrooctyl-l-trichlorosilane vapor.
  • PDMS Sylgard 184 A and B in 5:1 ratio
  • the PDMS was cured at 70 0 C for 2 hours and peeled off from the master. Holes were punched for access to the channels.
  • the PDMS layer was then bonded onto the slide glass by oxygen plasma.
  • the device comprises a parallel channels with a pillar array -25-30 ⁇ m apart.
  • Deformable worms can path through the gap between pillars, but non-deformable debris bigger than the gap are filtered out.
  • the code for the worm sorting contains three basic elements: waiting for worm's entrance to detection zone, grabbing images and performing the image processing, and allowing the worm to exit before returning to the initial state.
  • the code for the entering and exiting is essentially the same for all the sorting experiments, and will be described first. The procedure for identifying and sorting the individual mutants is discussed separately and in greater detail.
  • the procedure for trapping a worm is identical regardless of the genotype and whether the screen is done at high or low magnification.
  • the valve that controls the side channels is opened to allow flow through the channel, while all the other valves are closed. Frames from the camera are continually grabbed and analyzed to determine the presence of an animal by the average pixel intensity over a threshold.
  • the valve that controls that channel is opened as well as the L-shaped positioning valve to expedite exiting.
  • frames from the camera are continually acquired, and once no animal is detected to be present in the channel, the exit channels and the L-shaped positioning valves are closed immediately while the side channels are opened.
  • high magnification (e.g. 10Ox) sorting only a fraction of the field of view is visible. In order to ensure that an animal has completely exited the imaging zone, a 300 ms delay is added to the routine before closing the exit channels.
  • CX6858 was as described below. To analyze the images, out-of-focus frames were discarded and the images are convolved with [1 1 -1] to accentuate small bright regions. A threshold was applied to determine the fluorescence from the intestine as well as AQR and PQR. Different thresholds are then applied to the left and right nematode centroid to identify AQR and PQR and to distinguish PQR from the intestine auto-fluorescence. Groups of remaining pixels are then compared based on a number of features (size, position, etc) to determine whether AQR and PQR are present, and if so, where they are located. For each animal, the most in-focus of the pictures is used to determine the intensity of AQR and PQR. The correctness of the output (the location and presence of AQR and PQR) for each animal was independently verified and corrected if necessary. The algorithm was found to have >95% accuracy.
  • Subcellular Imaging of CZ5261 and CZ5264 The sorting of strains CZ5261 and CZ5264 relies on determining the locations of GFP along the ventral nerve cord. This is done using the same methods as mentioned earlier, namely compressing the z-stack to the x-y plane and then convolving it with a matrix [2 0 0 0 -1.5] to accentuate the puncta. A threshold is subsequently applied to the image to locate the puncta and depending on the number of puncta present, the animals are determined to be either wild type or have a mutant background.
  • Example 2 Automated Rapid Imaging, Phenotyping, and Sorting of C. elegans in an Integrated Microsystem
  • FIG. 1 is a schematic of the microsystem functions in rapid imaging, phenotyping, and sorting a mixed population of animals based on cellular and sub-cellular phenotypes.
  • the hardware is comprised of a microfluidic device (fabricated in-house), a microscope and camera system, a motorized stage, valves, pressure controller, and a Peltier cooling system (Figure 6).
  • Figure 6 is a system block diagram showing the on-chip and off-chip components and features.
  • the integrated system is controlled by in-house programs coded in Matlab®.
  • the functionality of the software includes image acquisition, writing, processing, and recognition, as well as components to automate pressure control, stage control, and feedback. It is a self-contained and closed-loop system that needs minimal human intervention; in our experiments, the system was repeatedly left running unattended for hours.
  • Figure 3B is an exploded view of a schematic illustration showing the individual lithography layers of the microsystem.
  • Figure 2A is an optical micrograph of the central region of the microchip. Animals are freely moving in a pre-imaging chamber (not included and to the left of the shown portion of the chip in Figure 2A-B). A gentle pressure gradient along the microchannel can load an animal into the detection zone. Temperature control channel below and around the detection zone carries a working fluid, and in the same layer are the control valves and the sample-loading regulator valve. Between the control layer and the sample layer is a thin membrane that can deflect.
  • Figure 3C provides schematics of cross-sectional view of the sample-loading regulator valve.
  • the microchip has four design features that ensure its robust operation for an extended period of time. First, it automatically self-regulates the loading of nematodes by the sample-loading regulator design ( Figure 3C). Second, it automatically positions the nematodes in an identical position in the chip (so as to minimize the travel of the motorized stage and thereby reduce the processing time and increase the throughput).
  • both outlet channels are closed while the side positioning channels remain open ( Figure 5A), and a constant pressure source is used to drive the flow into the microchip.
  • Self-regulation of loading plays a role in high-resolution imaging and accurate sorting.
  • the flow resistance is increased.
  • the reduced flow rate lowers the pressure on a second animal at the sample-loading regulator located at the entrance of the imaging chamber to a point where it is not sufficient to push the second animal into the chamber ( Figure 4).
  • the pressure drop across the second animal becomes sufficient to push it into the imaging chamber.
  • the sample-loading regulator design was implemented by controlling the pressure on a partially closed valve (Figure 3C).
  • Figure 3C The sample-loading regulator design was implemented by controlling the pressure on a partially closed valve.
  • the side channels are also controlled by the partially closed positioning valve, similar to the loading regulator valve. Once the animal's nose or tail is positioned at the end of the channel, the hydrodynamic resistance of the positioning channels self-equalizes. As a result, the animal stops moving in the direction of the flow ( Figure 5B).
  • the valve on the positioning channel is opened to generate a pressure gradient to guide an animal into the observation chamber. This distribution of the pressure force minimizes mechanical stress on the animal.
  • One advantage of the design of both the positioning valve and the sample-loading regulator in the main channel is that there are no permanent small features ( ⁇ 20 ⁇ m).
  • the dimension of the channels is not from the mask design but from the partially closed valves, and therefore is tunable and can be expanded if necessary. Because pieces of debris smaller than the size of the nematodes cannot be easily filtered out, this design feature prevents clogging of the channels - the valves can be opened to remove the debris when necessary.
  • a coarse microfluidic filter chip upstream from the imaging and sorting chip was employed.
  • Anesthetics in some cases may alter animals' metabolisms, growth, and phenotypes of interest.
  • immobilization plays a role for imaging at cellular and more specifically at subcellular resolutions.
  • an on-chip temperature control scheme is used in conjunction with a pressure gradient through the side channels.
  • the animal is cooled, immobilized, and imaged (Figure 5C) before being phenotyped and sorted accordingly ( Figure 5D).
  • the animal is cooled to 4 0 C, the animal remains still for the duration of image acquisition and processing.
  • Cooling is achieved by flowing salt solution of -8 0 C to -3 0 C on-chip through a large heat-exchanging channel (fabricated in the control layer of the device) beneath the observation chamber where the animal is positioned ( Figure 2A).
  • the salt solution is flown off-chip and chilled through small metal tubes adjacent to a Peltier cooler ( Figure 6); by varying the voltage applied to the Peltier cooler, the salt solution temperature can be precisely controlled.
  • the temperature in the observation chamber on-chip has been calculated to be about 4 0 C at the experimental conditions. Animals were observed to become still in the chamber almost instantaneously due to their small thermal mass, and immediately regained their typical thrashing motion upon exiting the cooled observation chamber.
  • the chip is microfabricated using well-established multilayer soft lithography techniques with some modifications.
  • the device is made of silicone elastomer polydimethylsiloxane (PDMS), which is optically transparent, exhibits negligible auto- fluorescence, and is elastic so micro on-chip valves can be built into the structure.
  • PDMS silicone elastomer polydimethylsiloxane
  • the device is capped with a standard microscope coverglass to ensure compatibility with all types of microscopes and objectives.
  • Devices built with a conventional multilayer process have the control layer (where gas is pressurized to actuate the valve membrane) between the sample-handling layer and a glass substrate in order to have a fully closed channel. Light has to pass through both the coverglass and the control layer when microscopy is performed, which may pose a limitation for the sample thickness at high magnification.
  • the present microsystem is capable of interfacing with a wide variety of microscope and camera systems, and thus would be a relatively inexpensive addition to the experimental facilities typically present in a biology laboratory.
  • the microscope Leica DM4500
  • camera Haamamatsu C9100-13
  • motorized stage Applied Scientific Instrumentation MS -4000 XYZ
  • Dim fluorescent reporters and subcellular features require high magnification and high numerical aperture lenses and a sensitive camera, while bright reporters and relatively larger features do not need such expensive equipment.
  • the end-user of our system will pick the microfluidic chip of the appropriate geometry and the software modules of appropriate capabilities with the microscope, camera, and stage of choice. To automate the operation of the microsystem, a series of software modules were developed.
  • the specific module to be used depends on the imaging and sorting applications at hand.
  • the software controls image acquisition and processing, stage movement, and opening and closing of the on-chip valves through off-chip macro valves.
  • An automated operation cycle of the microchip is demonstrated in Figures 5E-G, which are optical micrographs showing automated imaging and sorting sequence.
  • the camera and stage are then controlled to grab a series of images that cover the three-dimensional volume the animal occupies; the images are then stored and, if sorting is desired, are processed in real time to determine the animal phenotype and select the proper exit channel by triggering the corresponding outlet valve to open.
  • the program waits until the animal leaves the observation chamber. The exit channel is then closed, and this completes a cycle.
  • the processing cycle starts over again. This sequence of events usually happens within a few seconds, depending on the sophistication of the image processing algorithm.
  • the individualized image processing modules take advantage of a priori knowledge of the phenotypes of the strains; in some cases the software is further fine-tuned in real time by examining the animals in the device at the beginning of each application to achieve high-speed processing.
  • modified FACS has large throughput, but the images are only 1-D (e.g., average intensity in the dorsal-ventral left-right plane) and the resolution is on tissue scale.
  • finding the expression pattern accurately as well as quantifying the expression level is important.
  • C. elegans strain CX6858 contains an integrated transgene kyh342 [pgcy-32::tax-4::gfp, punc-122::gfp].
  • Green fluorescence protein is expressed in at least some of the following sensory neurons that normally express a soluble guanylyl cyclase gene gcy-32: AQR and URXL/R in the head and PQR in the tail.
  • the expression pattern and levels in AQR and PQR vary from individual to individual.
  • GFP is also present in other cells through the expression of a coinjection marker (punc-122::gfp in coelomocytes).
  • the present example shows that neurons can be identified and distinguished, gene expression levels can be quantified, and the animals can be phenotyped.
  • Figure 8B-E show representative raw images for each of the four possible expression patterns (in URXs only, in AQR and URXs, in PQR and URXs, and in all four cells) in the microdevice (Scale bar: 100 ⁇ m).
  • Figure 8B shows GFP expressed in URXL/R only.
  • Figure 8C shows GFP expressed in AQR and URXL/R.
  • Figure 8D shows GFP expressed in PQR and URXL/R.
  • Figure 8E shows GFP expressed in AQR, POR, and URXL/R.
  • FIGS. 8F-I show the processed images where gut auto-fluorescence, coelomocyte GFP, and GFP in URXL/R were filtered out by the software, only leaving AQR and PQR fluorescence.
  • Figures 8J-M show the overlay of the raw images B-E and the processed images F-I.
  • Figure 8N graphically depicts the percentage of animals with each of the possible expression patterns in >l,000 animals.
  • quantification of the GFP expression level was performed on AQR and PQR.
  • AQR and PQR are putative oxygen- sensing neurons in the head and in the tail respectively, their distinct expression levels may explain the individual variations in behavior in an oxygen gradient.
  • Figure 80 shows the histogram of the loading time for the individual animals and over 58% of animals were loaded in the observation chamber within 1 sec.
  • the loading scheme of the present example is passive and therefore very simple; furthermore, this experiment demonstrated that the loading is also fast and efficient.
  • the technique of phenotyping of the present example compared to manual methods, is high-throughput, it minimizes photobleaching and ensures uniformity of treatment on the samples, and therefore it is able to produce imaging data that are quantitative.
  • Figure 9 illustrates automated three- dimensional imaging and sorting with cellular resolution in the microchip.
  • Figure 9 A-D are representations of an image processing and decision-making process to sort animals based on the number of AWC neurons expressing pstr-2::GFP (the AWC-ON cells).
  • Several series of sparse z-stack images along the body of each animal were obtained and analyzed to determine the location of the head where fluorescence is most intense (Figure 9A).
  • a mixed population of the two genotypes was successfully sorted based on the GFP patterns.
  • Age-synchronized adult animals of both strains were mixed at a ratio of -1.5 % slo-1 mutant in wild-type background and processed in the microsystem.
  • a total of about 300 animals were sorted in about 6 hours continuously. Collected animals were 100% viable and behaved normally on agar plates with bacterial food. The accuracy was verified by both scoring the recorded images and also examining the collected animals by behavior for roller phenotype since the strain that carries slo-1 mutation also has rol-6 as a coinjection marker. All but one 2-ON animals were sorted correctly, and the 2-ON animals were enriched by > 25 fold.
  • the false positive rate (1 -ON-I -OFF animals being sorted as 2-ON animals) is ⁇ 2%.
  • the images in the experiments were recorded and can be retrieved if further analysis is required. For example, it is trivial to quantify the distribution of the absolute gene expression level (i.e. intensity of GFP) in the AWC-I-ON animals.
  • the microsystem is compatible with any microscope system, one can use deconvolution or confocal techniques if higher image quality is required for a particular application.
  • strains CZ5261 and CZ5264 are of wild-type background and carries an integrated reporter transgene julsl98 [punc-25-YFP::rab-5], which expresses YFP-RAB -5 in the cell bodies of the GABAergic motorneurons in C. elegans ( Figure 10A-D).
  • Strain CZ5264 also carries the marker transgene julsl98 but is mutant in its genetic background.
  • CZ5264 has an altered phenotype in the marker intensity in the cell bodies and along the nerve cord ( Figure 1 OE-H).
  • Age- synchronized animals were cultured and mixed at a ratio of about 30 % mutants to about 70% wild-type background. Greater than 1,300 animals were sorted in 7 hours continuously without interruptions, showing the robustness of the device and the approach.
  • the software program was able to identify cell bodies as well as the puncta phenotype along the ventral nerve cord of the animals.
  • a threshold is applied and groups of pixels above the threshold are counted to determine the number of cell bodies and puncta present. Over 99.9% of the animals were viable and behaved normally on culture plates after sorting.
  • the animals were collected and examined behaviorally, in addition to verifying the recorded image sets.
  • the overall sorting accuracy was 97.7%.
  • the present system can be easily set up as a multiple-pass sorting scheme to further increase the sorting efficiency or to sub-categorize the previously sorted animals.
  • the sorting speed can be even further improved by improving the algorithm as well as upgrading the computer hardware to improve the speed for data writing.
  • Figure 1OK shows the effect of photobleaching on samples, which can be minimized by using the present automated system. After 20 seconds of exposure to the excitation light, the samples are much dimmer. If the same thresholding criteria are used, the number of puncta can be easily miscounted (or puncta miscategorized), and quantification of the puncta brightness can be noisy.
  • the two sets of arrows in Figure 1OK point to two small puncta structures that may not be identified had the sample been photobleached for 20 seconds.
  • samples are only exposed to light once while the images are obtained, eliminating the need to focus and find targets by a skilled expert. Therefore the treatment of all samples is equal, and the bias from the operator is minimal. For screens on many synaptic markers, photobleaching is also a concern. This microsystem and the automated approach can be used to enable faster discovery of molecules and pathways at such subcellular resolutions.
  • the automation and computer control of the microsystem was created using the Mathworks software MatlabTM. Using the MatlabTM environment, custom programs and algorithms were created to control the three primary systems: (1) a camera, (2) the XYZ stage, and (3) the off-chip solenoid valves.
  • the program controls the various camera functions and settings such as exposure time, sensitivity, gain, image grabbing, and logging.
  • the XYZ stage is controlled via the COM port. It is actuated in the z-axis to acquire images at multiple focal planes, and in the xy-plane to acquire images along the anterior/posterior and the ventral/dorsal axes of the worm.
  • a simple digital I/O board is initialized and used to selectively turn on/off the individual valves that expose the on-chip micro-valves to either an ambient air pressure, or high pressure. In this manner the on-chip valves are turned on or off.
  • the digital I/O board actuates the valves to open the suction valve and close all others on-chip.
  • Images from the camera are constantly acquired during this time.
  • the program waits until a certain number of pixels are above a threshold. Once 10 pixels are above this threshold, a worm is assumed to be within the field of view of the camera.
  • the xyz- stage is actuated in the z-axis to acquire images at multiple focal planes (this is called a z- stack). Images are acquired based on the step size in the z direction and the total distance desired to be covered by the z- stack.
  • the xyz-stage may be moved in the xy-plane so that additional z-stacks are acquired at different points along the worm. This step may or may not be necessary.
  • the z-stacks are processed to find the individual neurons or puncta where GFP is expressed. Due to the ability to of the system to completely stop the movement of the sample, animals can be sorted based on GFP expression patterns at the cellular and sub-cellular level. This process is extremely flexible, and numerous algorithms have been created to sort samples depending on their expression patterns. Examples of sorting criteria include but are not limited to number of GFP expressing neurons/puncta, intensity of the GFP expression, size of the neurons/puncta, and distance between adjacent neurons/puncta.
  • the worm is sent through either the left or right exit channels by using the digital I/O board to open the on-chip valve. During this time the camera continues acquiring images and once the images drop below a certain threshold (see step 2), the worm is assumed to have exited the field of view. The program then waits half a second to allow the worm to fully exit before closing the exit and returning to step 1.
  • GFP Global System for Mobile Communications
  • a low threshold is used and several image processing steps are taken, including but not limited to the filling of holes or the application of a look up table._Finally, bounding boxes are found along each edge of the worm (used as the neurons are along the ventral nerve cord) and a threshold based on the mean plus one standard deviation is applied to get the following image.
  • the worms have numerous puncta along the nerve cord, and thus, this example is interested in imaging and locating these subcellular features. Because the features are extremely small, have limited fluorescence and are not necessarily localized to a single focal plane, a different method of locating the puncta was selected. Instead of selecting a single image out of the z-stack as with the previous method, the information in the z-stack was compressed. This was done by creating a matrix in the same size as a single plane of the z-stack. The value of the matrix at each x-y coordinate was set equal to the standard deviation in the z-axis at that x- y coordinate in the z-stack.
  • Example 4 Computer-enhanced high-throughput genetic screens of C. elegans in a microfluidic system Visual screens based on fluorescent markers are commonly used in genetics and drug discovery but when applied to multicellular organisms are currently limited in throughput and accuracy.
  • the present example provides a genetic screen of C. elegans on-chip, performed using computer-enhanced human decision-making. Animal handling streamlined by microfluidic devices and the control software enabled the identification of novel mutants and a large screening speed.
  • fluorescent reporter-based screens are common techniques.
  • C. elegans In the nematode C. elegans, often one is interested in changes to a specific phenotype based on morphology, including but not limted to reporter intensity, location, or patterns.
  • Current standard approaches to these screens include manual microscopy, which is slow and a commercial system with high throughput capacity but limited resolution.
  • microfluidics can greatly assist animal handling, and that it is possible to sort animals based on well-defined phenotypes.
  • the present example provides a computer-enhanced microfluidic screening system for complex phenotypical screens of C. elegans.
  • Figure HA the video feed is shown in the top left box, and image processing steps can be selected, applied and displayed in the boxes on the right. Animals are sorted as either wild-type or mutant by selecting the appropriate button. If an image is unclear, pictures can be acquired at multiple focal planes and processed using selected image processing modules. While animals clearly exhibiting no interesting phenotypes can be dismissed quickly, potential mutants can be examined in greater detail using the image processing modules on the same user-interface. When a worm is in the field of view, one of over forty combinations of image processing options can be selected and subtle phenotypes emphasized.
  • one option is to acquire a small z- stack of images at different focal planes (with user-determined step size and number), and either autofocus or flatten the z-stack before further processing the images. This significantly reduces the time relative to manual focusing of the microscope and searching for the reporter, and potentially avoiding photobleaching of the markers.
  • image-filtering options to accentuate features of interest, which tend to be dim or low in contrast to human eyes, but the phenotypes become more obvious with image enhancement.
  • Figure HB is a representative sequence of total processing time per animal, showing robust and easy animal handling and processing in the device. Animals of potentially interesting phenotypes are examined in detail, typically taking more than 4 seconds each (shaded in pink), while the majority of animals are processed in ⁇ 2 seconds.
  • the hardware system of the present example takes advantage of the higher magnification and higher numerical aperture of a compound microscope and the simple and streamlined animal handling of a novel microfluidic device.
  • the chip comprises a two-layer polydimethylsiloxane device with a positioning control valve and two outlets (Figure 2B).
  • Two different molds were fabricated by photolithographic processes to create worm loading layer and the control layer: a 30- ⁇ m-thick negative photoresist (SU8- 2025, Microchem) for the worm loading chamber and the detection channel and a 15- ⁇ m layer of negative photoresist (SU8-2010, Microchem) for the control layer.
  • Figure 2A provides an optical micrograph of the device active region.
  • the microfluidic device was fabricated using multi-layer soft lithography. Two different molds were fabricated by photolithographic processes to create worm loading layer and the control layer as follows: a 30- ⁇ m-thick negative photoresist (SU8-2025, Microchem) was spin-coated onto a silicon wafer for the worm loading chamber and the detection channel. The master for the control layer was made of a 15- ⁇ m layer of negative photoresist (SU8-2010, Microchem) on a silicon wafer.
  • SU8-2025 30- ⁇ m-thick negative photoresist
  • the master for the control layer was made of a 15- ⁇ m layer of negative photoresist (SU8-2010, Microchem) on a silicon wafer.
  • the two molds and a blank wafer were treated with tridecafluoro-lj ⁇ -tetrahydrooctyl-l-trichlorosilane vapor (United Chemical Technologies) in a vacuum desiccator to prevent adhesion of PDMS during the molding process.
  • PDMS polydimethylsiloxane
  • Sylgard 184 Dow Corning A and B in 10:1 ratio
  • 10:1 was then spin-coated onto the control layer to create a 50- ⁇ m-thick membrane.
  • the control layer was then allowed to relax at room temperature for 30 min.
  • the flow layer was partially cured at 70 0 C for 20 min, and the control layer at 65 0 C for 9 min.
  • the thick flow layer was then peeled off from the master, cut into small rectangles and individually aligned and bonded to the thin PDMS membrane on the control layer.
  • This assembled device was fully cured at 70 0 C for 2 hours. Once cured, the devices were removed from the wafer, and holes were punched to provide access to the various layers. Devices were then treated with oxygen plasma and irreversibly bonded to glass slides.
  • C. elegans were cultured according to established methods.
  • Mutagenesis was performed on age- synchronized L4 animals using EMS according to standard protocols.
  • F2 eggs were obtained by bleaching Fl adults using a solution containing about 1% NaOCl and 0.1 M NaOH, washed in M9 buffer, and cultured on Nematode Growth Medium (NGM) plates seeded with E. coli OP50 until L4 stage.
  • Animals were washed and suspended in M9 solution containing 0.02 wt% Bovine Serum Albumin (BSA) for each experiment. Animals were screened under a compound microscope at 20X based on differences in the reporter expression pattern or intensity; potential animals of interest were sorted into the mutant outlet and were collected directly from tubing connected to the mutant outlet with M9 solution containing 0.02 wt% BSA.
  • BSA Bovine Serum Albumin
  • the device has a minimum feature size of 30 ⁇ m to reduce the likelihood of clogging. Because of the overall simplicity and large tolerance built into the design to minimize the consequences of poor feature registration (either rotational or translational), the device can be easily duplicated by users unfamiliar with microfluidics.
  • the software interface allows users to control various camera settings such as sensitivity and gain, and to control the exit time of animals. By selecting the appropriate buttons, an animal is sorted as either mutant or wild-type. If the image in the streaming video window is unclear to the user, selecting "stack" will acquire images at multiple focal planes (number of and spacing of images as specified by the user). Images can be flattened and processed according to the user selection.
  • the various options for flattening the z-stack are: (1) summation (this flattens the stack by making each x-y point equal to the summation of the values at that point over the z-direction); (2) maximum (this flattens the stack by making each x-y point equal to the maximum of the values at that point over the z-direction); (3) standard deviation (this flattens the stack by making each x-y point equal to the standard deviation of the values at that point over the z-direction); and (4) in-focus (this assumes that the slice with the highest standard deviation is the most in-focus and uses it for the subsequent image processing steps).
  • the various options for image processing the flattened image are: (1)
  • Gaussian (applies a rotationally symmetric Gaussian low-pass filter to the flattened image); (2) Laplacian (applies a filter approximating the Laplacian operator to the flattened image); (3) Laplacian of Gaussian (applies a rotationally symmetric Laplacian of Gaussian filter to the flattened image); (4) Prewitt H (applies the prewitt filter for emphasizing horizontal edges to the flattened image); (5) Prewitt V (applies the prewitt filter for emphasizing vertical edges to the flattened image); (6) Sobel H (applies the sobel filter for emphasizing horizontal edges to the flattened image); (7) Sobel V (applies the sobel filter for emphasizing vertical edges to the flattened image); (8) unsharp (applies an unsharp filter for contrast enhancement created by the negative of a Laplacian filter and applies it the flattened image); (9) range (filters the image provided by the flattening step using the local range of the image); (10)
  • Figures 12 C, F, I, and L depict a mutant showing reduced YFP expression.
  • Figures 12A-C are images of animals that entered, not necessarily in focus and potentially rotated, resulting in an unclear image of the region of interest.
  • Figures 12D-F are images determined to be in-focus by computer after a series of images at different focal planes was acquired.
  • Figures 12G-I are selected alternative methods of viewing z-stack by flattening the matrix of images. Specifically Figure 12G demonstrates flattening by taking the standard deviation of the z-stack at each x-y location.
  • Figure 12H demonstrates flattening using the maximum value at each x-y location.
  • Figure 121 demonstrates flattening by taking the summation in the z-direction at each x-y location.
  • Figures 12J-L depict application a few of the image processing features to the flattened image to accentuate different features: Laplacian filter (12J); Unsharp filter (12K); and Laplacian of Gaussian filter (12L).
  • scale bars are 30 ⁇ m.
  • the computer-enhanced microfluidic approach demonstrated in the present example has many advantages: (1) computer-assisted screening to accentuate phentoypical characteristics, which may be missed by manual screens; (2) human decision-making to allow flexibility if presented with a novel phenotype; (3) preconfigured image processing modules for minimal algorithm-development time; (4) at least one or two orders of magnitude greater throughput than current manual screening; (5) higher magnification, higher numerical aperture optics than commercial or some manual screening systems; (6) almost three orders of magnitude less expensive than commercial systems; (7) simple assembly and operation for use by technicians with little or no familiarity with microfluidic s, among others. These advantages should enable new types of screens in the near future.

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Abstract

L'invention concerne de manière générale des dispositifs, des systèmes et des procédés pour une détection et un classement à haut débit. Plus particulièrement, les divers modes de réalisation de la présente invention sont destinés à des systèmes microfluidiques et à des procédés d'imagerie à haute résolution et de classement à haut débit. Certains modes de réalisation des dispositifs, des systèmes et des procédés de la présente invention peuvent concerner un dispositif microfluidique à passage unique pour une détection individuelle et un classement d'une pluralité d'organismes multicellulaires, tels que Caenorhabditis elegans, présentant au moins un phénotype, le système ayant une précision supérieure à environ 95 %.
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US8906905B2 (en) 2009-08-03 2014-12-09 University of Pittsburgh—of the Commonwealth System of Higher Education Methods of treating disorders associated with protein polymerization
WO2016063199A1 (fr) 2014-10-20 2016-04-28 Ecole Polytechnique Federale De Lausanne (Epfl) Dispositif microfluidique, système et procédé permettant l'étude d'organismes
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US10052631B2 (en) 2013-03-05 2018-08-21 Board Of Regents, The University Of Texas System Microfluidic devices for the rapid and automated processing of sample populations
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RU220009U1 (ru) * 2023-04-05 2023-08-21 Полина Андреевна Монахова Устройство для автоматической сортировки и визуализации биологических объектов
JP2024507723A (ja) * 2021-02-03 2024-02-21 ディスラプティブ テクノロジカル アドバンシス イン ライフ サイエンス エッセ．エッレ．エッレ． － ソシエタ’ ベネフィット、イン フォルマ アブレヴィアータ、ディテールズ エッセ．エッレ．エッレ．エッセビ がん検出方法
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US9511074B2 (en) 2009-08-03 2016-12-06 University of Pittsburgh—of the Commonwealth System of Higher Education Methods of treating disorders associated with protein polymerization
US8809617B2 (en) * 2009-11-05 2014-08-19 The University of Pittsburgh—Of the Commonwealth System of Higher Education Automated high-content live animal drug screening using C. elegans
US20110154510A1 (en) * 2009-11-05 2011-06-23 Pak Stephen C Automated high-content live animal drug screening using c. elegans
US9452171B2 (en) 2009-11-05 2016-09-27 University of Pittsburgh—of the Commonwealth System of Higher Education Methods of treating disorders associated with protein aggregation
US9820990B2 (en) 2009-11-05 2017-11-21 The University of Pittsburgh—Of the Commonwealth System of Higher Education Methods of treating disorders associated with protein aggregation
US9844605B2 (en) 2009-11-05 2017-12-19 The University of Pittsburgh—Of the Commonwealth System of Higher Education Transgenic Caenorhabditis elegans comprising a human protein with a tendency to aggregate fused to a fluorescent protein
US10052631B2 (en) 2013-03-05 2018-08-21 Board Of Regents, The University Of Texas System Microfluidic devices for the rapid and automated processing of sample populations
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US11596945B2 (en) 2014-10-20 2023-03-07 Ecole Polytechnique Federale De Lausanne (Epfl) Microfluidic device, system, and method for the study of organisms
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EP3505638A1 (fr) 2014-10-20 2019-07-03 Ecole Polytechnique Federale De Lausanne (EPFL) EPFL-TTO Dispositif microfluidique, système et procédé permettant l'étude d'organismes
US12179237B2 (en) 2015-07-16 2024-12-31 Sortera Technologies, Inc. Classifying between metal alloys
US12390838B2 (en) 2015-07-16 2025-08-19 Sortera Technologies, Inc. Sorting between metal alloys
US12280403B2 (en) 2015-07-16 2025-04-22 Sortera Technologies, Inc. Sorting based on chemical composition
US12103045B2 (en) 2015-07-16 2024-10-01 Sortera Technologies, Inc. Removing airbag modules from automotive scrap
US11278937B2 (en) 2015-07-16 2022-03-22 Sortera Alloys, Inc. Multiple stage sorting
US11471916B2 (en) 2015-07-16 2022-10-18 Sortera Alloys, Inc. Metal sorter
US12030088B2 (en) 2015-07-16 2024-07-09 Sortera Technologies, Inc. Multiple stage sorting
US12194506B2 (en) 2015-07-16 2025-01-14 Sortera Technologies, Inc. Sorting of contaminants
US12290842B2 (en) 2015-07-16 2025-05-06 Sortera Technologies, Inc. Sorting of dark colored and black plastics
US11964304B2 (en) 2015-07-16 2024-04-23 Sortera Technologies, Inc. Sorting between metal alloys
US12208421B2 (en) 2015-07-16 2025-01-28 Sortera Technologies, Inc. Metal separation in a scrap yard
US11975365B2 (en) 2015-07-16 2024-05-07 Sortera Technologies, Inc. Computer program product for classifying materials
US10722922B2 (en) 2015-07-16 2020-07-28 UHV Technologies, Inc. Sorting cast and wrought aluminum
US12280404B2 (en) 2015-07-16 2025-04-22 Sortera Technologies, Inc. Sorting based on chemical composition
US12017255B2 (en) 2015-07-16 2024-06-25 Sortera Technologies, Inc. Sorting based on chemical composition
US12109593B2 (en) 2015-07-16 2024-10-08 Sortera Technologies, Inc. Classification and sorting with single-board computers
US10710119B2 (en) 2016-07-18 2020-07-14 UHV Technologies, Inc. Material sorting using a vision system
US11969764B2 (en) 2016-07-18 2024-04-30 Sortera Technologies, Inc. Sorting of plastics
US10625304B2 (en) 2017-04-26 2020-04-21 UHV Technologies, Inc. Recycling coins from scrap
US11260426B2 (en) 2017-04-26 2022-03-01 Sortera Alloys, hic. Identifying coins from scrap
CN107942083A (zh) * 2017-11-14 2018-04-20 东南大学 用于秀丽线虫的微流控电阻抗检测分选芯片、系统及方法
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JP7627471B6 (ja) 2021-02-03 2025-03-07 ディスラプティブ テクノロジカル アドバンシス イン ライフ サイエンス エッセ．エッレ．エッレ． － ソシエタ’ ベネフィット、イン フォルマ アブレヴィアータ、ディ － テールズ エッセ．エッレ．エッレ．エッセビ がん検出方法
JP2024507723A (ja) * 2021-02-03 2024-02-21 ディスラプティブ テクノロジカル アドバンシス イン ライフ サイエンス エッセ．エッレ．エッレ． － ソシエタ’ ベネフィット、イン フォルマ アブレヴィアータ、ディテールズ エッセ．エッレ．エッレ．エッセビ がん検出方法
RU220009U1 (ru) * 2023-04-05 2023-08-21 Полина Андреевна Монахова Устройство для автоматической сортировки и визуализации биологических объектов

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