[go: up one dir, main page]

WO2019133098A2 - Plate-forme microfluidique pour analyse à résolution temporelle de tissus et d'organismes - Google Patents

Plate-forme microfluidique pour analyse à résolution temporelle de tissus et d'organismes Download PDF

Info

Publication number
WO2019133098A2
WO2019133098A2 PCT/US2018/056790 US2018056790W WO2019133098A2 WO 2019133098 A2 WO2019133098 A2 WO 2019133098A2 US 2018056790 W US2018056790 W US 2018056790W WO 2019133098 A2 WO2019133098 A2 WO 2019133098A2
Authority
WO
WIPO (PCT)
Prior art keywords
embryos
tissue samples
tissue
microfluidic system
chips
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2018/056790
Other languages
English (en)
Other versions
WO2019133098A3 (fr
Inventor
Richard Novak
Youngjae CHOE
Bret NESTOR
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Harvard University
Original Assignee
Harvard University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Harvard University filed Critical Harvard University
Publication of WO2019133098A2 publication Critical patent/WO2019133098A2/fr
Publication of WO2019133098A3 publication Critical patent/WO2019133098A3/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
    • C12M23/00—Constructional details, e.g. recesses, hinges
    • C12M23/02—Form or structure of the vessel
    • C12M23/16—Microfluidic devices; Capillary tubes
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
    • C12M37/00—Means for sterilizing, maintaining sterile conditions or avoiding chemical or biological contamination
    • C12M37/02—Filters
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N5/00—Undifferentiated human, animal or plant cells, e.g. cell lines; Tissues; Cultivation or maintenance thereof; Culture media therefor
    • C12N5/06—Animal cells or tissues; Human cells or tissues
    • C12N5/0602—Vertebrate cells
    • C12N5/0603—Embryonic cells ; Embryoid bodies
    • C12N5/0604—Whole embryos; Culture medium therefor
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N2521/00—Culture process characterised by the use of hydrostatic pressure, flow or shear forces

Definitions

  • the present invention relates generally to microfluidic devices and systems for cell culture and/or cell assays, and, specifically, to a static well chip that is compatible with pipetting or robotic systems.
  • Whole organisms such as the Xenopus embryo, enable analysis of normal embryonic development, response to infection, and development of therapeutic countermeasures. As such, whole organisms are ideal for screening compounds as part of the therapeutic development pipeline, but current screens rely on mice and other larger animals that are not amenable to scaled-up screens of hundreds or thousands of embryos. Embryos from fish and amphibians are attractive model systems in that they are small (millimeter scale), easy to culture, and well-representative of human pathways. Although Xenopus and zebrafish embryos are now used by many companies and academic groups to screen drugs, most rely on genetic or other reporters of pathway function and typically are conducted as endpoint assays.
  • a microfluidic system includes an automated platform for culturing embryos with hyperspectral imaging.
  • the microfluidic system further includes a plurality of static chips on the automated platform for providing a fresh medium exchange with minimal embryo motion.
  • the plurality of static chips is compatible with traditional benchtop tools
  • a method is directed to culturing embryos with hyperspectral imaging via a microfluidic automated platform.
  • the method further includes providing a fresh medium exchange via a plurality of static chips of the microfluidic automated platform, the plurality of static chips being compatible with traditional benchtop tools.
  • the method further includes maintaining minimal embryo motion while providing the fresh medium exchange.
  • FIG. 1 illustrates an Xenopticon frog embryo platform.
  • FIG. 2 illustrates top views of a plurality of different static chip designs.
  • FIG. 3A shows distribution plots of standard error of position for the chip designs of FIG. 2.
  • FIG. 3B shows distribution plots of standard error of position for two of the chip designs of FIG. 2 with control and infected embryos.
  • FIG. 4 illustrates time-resolved tissue tracking
  • FIG. 5 shows a chart of regression of Xenopus metrics.
  • FIG. 6 shows a front view of a static frog chip design, according to one embodiment.
  • FIG. 7 shows an isometric view of the design of FIG. 6.
  • FIG. 8 shows a front view of a static frog chip design, according to another embodiment.
  • FIG. 9 shows an isometric view of the design of FIG. 8.
  • FIG. 10 shows a front view of a static frog chip design, according to yet another embodiment.
  • FIG. 11 shows an isometric view of the design of FIG. 10.
  • FIG. 12 shows a front view of a static frog chip design, according to yet another alternative embodiment.
  • FIG. 13 shows an isometric view of the design of FIG. 12.
  • FIG. 14 shows a front view of a static frog chip design, according to yet another alternative embodiment.
  • FIG. 15 shows an isometric view of the design of FIG. 14.
  • FIG. 16 shows a front view of a static frog chip design, according to yet another alternative embodiment.
  • FIG. 17 shows an isometric view of the design of FIG. 16.
  • FIG. 18 shows a front view of a static frog chip design, according to yet another alternative embodiment.
  • FIG. 19 shows an isometric view of the design of FIG. 18.
  • FIG. 20 shows a front view of a static frog chip design, according to yet another alternative embodiment.
  • FIG. 21 shows an isometric view of the design of FIG. 20.
  • FIG. 22 shows a system for imaging and analyzing embryos.
  • FIG. 23 A shows a false color image of a dead tissue sample.
  • FIG. 23B shows a false color image of a live tissue sample.
  • FIG. 24A shows a transformed hyperspectral image of the dead tissue sample of FIG. 23 A.
  • FIG. 24B shows a transformed hyperspectral image of the live tissue sample of FIG. 23B.
  • FIG. 25 shows a graph illustrating data obtaining from repeatedly imaging embryos over a time period.
  • FIG. 26 shows survival rate versus time curves for the embryos of FIG. 25.
  • FIG. 27 shows a method for imaging and analyzing tissue samples such as embryos.
  • the microfluidic platform of the present disclosure is generally designed as a static well chip that maintains certain features of previous designs, such as imaging of embryos from the side (more features), long term viability and normal development.
  • the microfluidic platform has a new design that focuses on an optimized static well design that combines lateral imaging with ease of handling by being compatible with standard pipettors, including multipipettors.
  • an automated microfluidic platform - the Xenopticon platform - is directed to culturing Xenopus laevis embryos with hyperspectral imaging for time-resolved data acquisition, as well as automated development metrics.
  • the Xenopticon platform is directed to understanding pathogen infection and response to therapeutic interventions, and is useful in observing inter-subject variability of response to Aeromonas hydrophila infection.
  • the Xenopticon platform is further helpful in demonstrating the use of an automated culture system for Xenopus embryo analysis that may be applicable to therapeutic development.
  • image analytics are added to process hyperspectral images (16 spectra) to identify tissue regions and track them during development.
  • the image analytics are useful for assessing embryo viability and predict outcomes well ahead of the actual time of death.
  • the microfluidic platform is useful for measuring impact of interventions, including drugs and drug candidates, on infection and other diseases. The measurements are helpful in providing data to develop therapeutic regimes in terms of time and dose ranges to more quickly allow a drug to enter clinical testing.
  • Static wells/chips include a thermoplastic microfluidic/mesofluidic device embossed or injection-molded to form an embryo culture region and a medium reservoir separated by small posts. Static wells/chips are optimized by measuring embryo position in the chips over development, and the best chips minimized motion of embryos. Posts are designed to contain embryos while providing adequate medium diffusion to the embryo, allowing culture of chips for several days without having to replenish medium with no loss of viability. A gas permeable membrane is used to seal the device, which is useful for static chips because there is no fluid perfusion that would otherwise deliver oxygen and remove waste/C02.
  • tissue tracking is done by analyzing hyperspectral images to obtain regions of similar“color.” These regions and geometric features are processed using various algorithms to obtain hundreds to thousands of unique metrics per embryo that can be measured over time. Unique combinations of features are used to define tissue types of interest. This allow analysis of phenotypes and tissues in wild-type embryos without any manipulation of any kind.
  • the Xenopticon platform 10 has the capability to automatically image up to 672 individual embryos in less than 15 min in 16 colors.
  • the platform 10 generally includes a hyperspectral camera or microscope 12, a thermoelectric cooler 14, a plurality of chips 16, a chip manifold 18 to hold the plurality of chips 16, and a backlight 20.
  • the plurality of chips 16 can include perfused chips 22A and/or static chips 22B.
  • Perfused chips 22A provide fresh medium exchange with minimal embryo motion for accurate imaging over one week of development.
  • Static chips 22B offer greater ease of use and compatibility with traditional benchtop tools.
  • Static chips 22B are fabricated in polycarbonate or styrene-ethylene-butadiene styrene elastomer via hot embossing. An air permeable membrane seals the chips 16 and provides adequate gas exchange for embryo respiration. Both chip designs enable normal embryo development for up to one week.
  • FIG. 3A shows graphs 26A-26F of the standard error of position for the different static chip designs 24A-24F.
  • FIG. 3B shows graphs 28A and 28B of the standard error of position of control and infected embryos for static chip design 24B, and graphs 28C and 28D of the standard error of position of control and infected embryos for static chip design 24D.
  • Static chip design optimization is generally illustrated, with static well/chip designs optimized using the imaging system to minimize embryo motion over several days while providing adequate growth conditions and efficient sample handling.
  • time-resolved tissue tracking is directed to hyperspectral image stacks that are parsed into the separate 16 colors, with 2D regions identified using the K- nearest neighbor algorithm, and 100s- 1, 000s of unique metrics are generated to describe tissue properties.
  • Unique tissue signatures enable tracking of tissue regions during development without the need for transgenic reporters or other labels, such as the gut tracking time lapse shown here. Imaging can also be used to track infection. The time lapse on the right shows A. hydrophila infection of an embryo.
  • an automated development assessment shows a time course of development of 14 embryos demonstrating discrimination of live and dead embryos.
  • Control embryos 30A (white markers with black dashes inside) develop normally (4/4), while 5/5 infected embryos 30B (white markers with nothing inside) die.
  • Embryos 30C treated using deferoxamine mesylate (DFOA) (black markers with white dashes inside) survive at a rate of one embryo out of five.
  • Support vector machine classification automates live/dead determination with >96% accuracy.
  • the Xenopticon platform provides automation of embryo handling and a time-resoled analytical framework for studying Xenopus embryo development. Additionally, observation of distinct infection response time courses for individual embryos in treated and untreated infections may provide further insight into pathogen and therapeutic dynamics.
  • FIGs. 6-21 different chip designs are illustrated in accordance with the above-described features.
  • FIGs. 6 and 7 show front and isometric views of chip design 32.
  • FIGs. 8 and 9 show front and isometric views of chip design 34.
  • FIGs. 10 and 11 show front and isometric views of chip design 36.
  • FIGs. 12 and 13 show front and isometric views of chip design 38.
  • FIGs. 14 and 15 show front and isometric views of chip design 40.
  • FIGs. 16 and 17 show front and isometric views of chip design 42.
  • FIGs. 18 and 19 show front and isometric views of chip design 44.
  • FIGs. 20 and 21 show front and isometric views of chip design 46.
  • a system 100 for imaging and analyzing embryos includes a platform 102 for culturing the embryos, a plurality of chips 104 disposed within the platform for storing the embryos during incubation and keeping the embryos stationary, a hyperspectral camera 106, and one or more processing devices 108.
  • the system is configured to periodically image all of the embryos that are stored within the chips with the hyperspectral camera. In some implementations, the system can image each individual embryo once every fifteen minutes.
  • the one or more processing devices can be configured to perform a variety of analytical techniques to analyze the embryos, identify and track infected tissue within the embryos, determine whether the embryos are alive or dead, and monitor the responses of the embryos to a variety of biological processes and stimuli.
  • FIGS. 23A and 23B show a false color image 48A of a dead embryo and a false color image 48B of a live embryo in respective chips during and/or after incubation.
  • FIGS. 24A and 24B show a transformed hyperspectral image 50A of the dead embryo and a transformed hyperspectral image 50B of the live embryo, respectively.
  • the hyperspectral images can be obtained using the hyperspectral camera, and generally are 16-color images. Each pixel in the hyperspectral images can have up to 16 different values.
  • the hyperspectral images can be used to analyze the severity of any infection present in the embryos. As shown in FIGS.
  • the transformed hyperspectral images 50A, 50B of the embryos can include a scale 52 to indicate whether the tissue at a given location in the image is infected.
  • the transformed hyperspectral images 50A, 50B are analyzed to determine what percentage of the tissue of the embryo is infected. An amount of infected tissue above an infection threshold can indicate that the embryo is dead, while an amount of infected tissue below the infection threshold can indicate that the embryo is alive.
  • an infection threshold of 50% can be used to delineate between live embryos and dead embryos.
  • a determination that at least 50% of the tissue in the embryo is infected results in the embryo being classified as dead, while less than 50% of the tissue being infected results in the embryo being classified as alive.
  • This binary analysis of the image scan generally be carried out by the one or more processing devices, or can be carried out by a human operator.
  • the one or more processing devices can automatically utilize more complex image analysis techniques.
  • a classifier can be trained to look for infected tissue in the hyperspectral images and determine whether the embryo is alive or dead. The classifier generally looks at every pixel in the image that represents a portion of the embryo and determines whether the color of the pixel indicates an infection or not.
  • a hidden Markov statistical model can be applied to link image time series together. In these implementations, a variety of assumptions can be made about the survival and death of the embryos can be made. For example, one assumption of the classifier is that a truly dead embryo will not come back to life. The classifier can generally determine the viability of the embryos with about 93% accuracy.
  • FIG. 25 shows a graph 54 illustrating data obtained from repeatedly imaging eight different embryos or sets of embryos over a time period exceeding 100 hours. Specifically, the graph 54 in FIG. 25 shows the fraction of the tissue of the embryo that is infected.
  • the legend shows that the different embryos or sets of embryos each have a different number of colony forming units, e.g., the different sets of embryos were generally infected with a different amount of a pathogen (such as bacterial cells, fungal cells, or infectious agents).
  • Embryos containing greater numbers of colony forming units i.e. embryos that were infected with a larger amount of pathogen generally had a larger percentage of tissue that was infected at a given point in time, confirming the viability of the model.
  • FIG. 26 shows two graphs that illustrate survival rate vs. time curves for the eight sets of embryos of FIG. 25.
  • Each of the curves plot the percentage of the embryos in each set that is considered to be still alive against the amount of time elapsed since the embryo became infected.
  • the graph on the left shows the survival curves of the eight sets of embryos based on a human estimating whether or not the embryos are dead or alive at each point in time.
  • the graph on the right shows the survival curves for the same eight sets embryos that were analyzed using the classifier with the hidden Markov model discussed herein.
  • the percentage of embryos still alive in each set drops off more rapidly for embryos having higher concentrations of colony forming units, e.g., embryos that were initially infected with a larger amount of a pathogen.
  • the classifier generally results in a more accurate survival curve with a finer time resolution.
  • the system 100 can more accurately determine whether any given embryo is alive at a certain point in time. This allows the time of death of any embryos to be determined much more precisely than previous implementations. In some implementations using the automated classifier, the time of death of the embryos can be determined to the nearest 15 minutes.
  • the classifier is also more accurate than a human estimator, as the model resulted in an accuracy of about 90.0% as compared to an accuracy of about 84.2% with a human estimator.
  • the automated classifier also had a true positive rate of about 93.5% and a true negative rate of about 89.8%.
  • FIG. 27 shows a method 200 for imaging and analyzing tissue samples such as embryos.
  • a plurality of chips are provided within a platform.
  • the chips can be any of the chips disclosed herein.
  • the chips can include static chips or perfused chips.
  • the chips can also include any of chips 32, 34, 36, 38, 40, 42, 44, or 46.
  • a plurality of embryos are incubated within the plurality of chips. Generally, each individual embryo is incubated in its own respective chip.
  • a plurality of images is obtained of each embryo for each of a plurality of points in time.
  • the embryos are continually imaged over a certain time period.
  • the plurality of embryos is repeatedly and sequentially imaged over 15-minute time periods such that each embryo is imaged every 15 minutes.
  • the first embryo in the sequence may be imaged at 0 minutes, 15 minutes, 30 minutes, 45 minutes... until the end of the analysis. Other time periods can also be used.
  • the images of the embryos are hyperspectral images captured with a hyperspectral camera.
  • the hyperspectral images are analyzed to identify infected and non-infected tissue in each of the embryos. For a given embryo, the images at each point in time are analyzed to identify infected and non-infected tissue for each point in time.
  • step 210 it is determined whether each embryo is alive or dead at each point in time that the images of the embryos were obtained, based on the analysis of the images and the amount of infected and/or non-infected tissue. This determination can be made by a human visually analyzing the images, the one or more processors 108 of the system 100, or any combination. Moreover, any suitable method of determining whether each embryo is alive or dead can be utilized, including the methods disclosed herein.
  • survival characteristics for the embryos can be determined.
  • the plurality of embryos is separated into at least two different portions for comparison purposes.
  • a first set of survival characteristics for a first portion of the embryos can be determined.
  • a second set of survival characteristics for a second portion of the embryos can be determined.
  • the first set of survival characteristics and the second set of survival characteristics are compared.
  • the survival characteristics can be any attribute of the embryos that one wishes to measure and compare.
  • the survival characteristics may be survival curves such as those illustrated in FIG. 26.
  • the survival characteristics may measure the response of the embryos to one or more stimuli, such as infection, treatment, light/dark, heat, humidity, etc. The survival characteristics do not necessary relate spec.
  • the first and second portions of the plurality of embryos may include embryos treated with an infectious agent (such as a pathogen, bacteria, fungus, etc.) and embryos treated with both an infectious agent and a treatment agent.
  • an infectious agent such as a pathogen, bacteria, fungus, etc.
  • embryos treated with both an infectious agent and a treatment agent By monitoring the infection in the tissues of the embryos and determining whether each embryo is alive or dead at each point in time, a user and/or the system 100 can produce sets of survival characteristics that are effected by the infectious agent and the treatment agent. Thus, the user and/or the system 100 can monitor the effect the treatment agent has on infected embryos.
  • the plurality of embryos may include additional portions.
  • a third portion of the embryos may be a control portion that does not have an infectious agent or a treatment agent introduced thereto.
  • Additional portions may include different types of treatment agents to analyze different potential treatments for the same infectious agent, or different types of infectious agents to analyze the effect a given treatment agent has on different infectious agents.
  • an infectious agent and a treatment agent may be introduced to embryos in both a first portion of the plurality of embryos and a second portion of the plurality of embryos.
  • the first portion of the plurality of embryos can be illuminated during analysis, while the second portion of the plurality of embryos can be kept in the dark during analysis.
  • the user and/or the system 100 can monitor the effect that illumination has on the efficacy of the treatment agent.
  • the time when a treatment agent is administered to different portions of the plurality of embryos can be altered so as to determine whether small shifts in treatment timing effect clinical outcomes.
  • the treatment agent can be introduced to both the first and second portions of the plurality of embryos under different conditions and/or the first and second portions of the plurality of embryos can be incubated, imaged, and analyzed under different conditions to determine the effects on the efficacy of the treatment agent.
  • tissue samples other than embryos may be used, such as eyes, muscles, organs, or any suitable type of tissue.
  • the hyperspectral images captured by the hyperspectral camera can be utilized to isolate other metrics of interest without having to re-do any experiments. Generally, any of the implementations described herein may be combined as needed.
  • the system 100 can be configured to enable generation of very high content data for large numbers of embryos/tissue samples in parallel with a fine time resolution. In some implementations, the capacity of the system 100 is approximately 700 embryos with a 15 minute imaging time period. Implementations with fewer embryos can provide an even finer time resolution less than 15 minutes.
  • a large number of embryos can be analyzed at once, and their times of death can be determined to the nearest 15 minutes.
  • This l5-sminute resolution can generally change with the number of embryos being analyzed at once, as fewer embryos results in less time to capture a hyperspectral image of each embryo, meaning that any given embryo can be imaged more often.
  • This provides the system with the capability to track the response of an embryo to a variety of different rapid biological processes.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Organic Chemistry (AREA)
  • Wood Science & Technology (AREA)
  • Zoology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Biomedical Technology (AREA)
  • Biotechnology (AREA)
  • Genetics & Genomics (AREA)
  • Biochemistry (AREA)
  • General Engineering & Computer Science (AREA)
  • Microbiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Developmental Biology & Embryology (AREA)
  • Reproductive Health (AREA)
  • Gynecology & Obstetrics (AREA)
  • Sustainable Development (AREA)
  • Cell Biology (AREA)
  • Molecular Biology (AREA)
  • Dispersion Chemistry (AREA)
  • Clinical Laboratory Science (AREA)
  • Apparatus Associated With Microorganisms And Enzymes (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

L'invention concerne un système microfluidique comprenant une plate-forme automatisée pour la culture d'embryons incluant une imagerie hyperspectrale. Le système microfluidique comprend par ailleurs une pluralité de puces statiques disposées sur la plate-forme automatisée et destinées à assurer un échange de substances fraîches avec un mouvement embryonnaire minimal. La pluralité de puces statiques sont compatibles avec des outils de référence conventionnels.
PCT/US2018/056790 2017-10-20 2018-10-19 Plate-forme microfluidique pour analyse à résolution temporelle de tissus et d'organismes Ceased WO2019133098A2 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201762575265P 2017-10-20 2017-10-20
US62/575,265 2017-10-20

Publications (2)

Publication Number Publication Date
WO2019133098A2 true WO2019133098A2 (fr) 2019-07-04
WO2019133098A3 WO2019133098A3 (fr) 2019-09-12

Family

ID=67066526

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2018/056790 Ceased WO2019133098A2 (fr) 2017-10-20 2018-10-19 Plate-forme microfluidique pour analyse à résolution temporelle de tissus et d'organismes

Country Status (1)

Country Link
WO (1) WO2019133098A2 (fr)

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6118890A (en) * 1997-11-12 2000-09-12 International Business Machines Corporation System and method for broad classification of biometric patterns
US8532931B2 (en) * 2008-09-07 2013-09-10 Edward Lakatos Calculating sample size for clinical trial
US20120044339A1 (en) * 2010-08-19 2012-02-23 Stith Curtis W Opto-fluidic microscope system with evaluation chambers
WO2014172688A1 (fr) * 2013-04-19 2014-10-23 California Institute Of Technology Manipulation d'échantillons en parallèle
US11596945B2 (en) * 2014-10-20 2023-03-07 Ecole Polytechnique Federale De Lausanne (Epfl) Microfluidic device, system, and method for the study of organisms
WO2017027838A1 (fr) * 2015-08-13 2017-02-16 President And Fellows Of Harvard College Dispositifs et systèmes microfluidiques pour culture cellulaire et/ou essai cellulaire

Also Published As

Publication number Publication date
WO2019133098A3 (fr) 2019-09-12

Similar Documents

Publication Publication Date Title
Dunker et al. Pollen analysis using multispectral imaging flow cytometry and deep learning
JP7744340B2 (ja) ハイスループット薬物スクリーニングのためのシステムおよび方法
US20230145084A1 (en) Artificial immunohistochemical image systems and methods
JP5878874B2 (ja) 生物学的有機体の時間関連顕微鏡検査のためのシステムおよび方法
JP7034354B2 (ja) 生存細胞の画像解析のための解析方法
Pennekamp et al. BEMOVI, software for extracting behavior and morphology from videos, illustrated with analyses of microbes
Frentz et al. Strongly deterministic population dynamics in closed microbial communities
US20100135566A1 (en) Analysis and classification, in particular of biological or biochemical objects, on the basis of time-lapse images, applicable in cytometric time-lapse cell analysis in image-based cytometry
KR20190080914A (ko) 세포 화상 평가 장치 및 세포 화상 평가 제어 프로그램
JP7641906B2 (ja) 評価対象の評価方法、画像処理装置及び評価対象の評価システム
Harrison et al. Evaluating the utility of brightfield image data for mechanism of action prediction
London et al. An automated system for rapid non-destructive enumeration of growing microbes
Maeda et al. Colony fingerprint for discrimination of microbial species based on lensless imaging of microcolonies
CN109661473A (zh) 用于确定微生物的存在和鉴定所述微生物的方法、系统和计算机程序产品
KR20230163541A (ko) 신속한, 자동화된 이미지 기반 바이러스 플라크 및 효능 검정법
WO2019133098A2 (fr) Plate-forme microfluidique pour analyse à résolution temporelle de tissus et d'organismes
Koch et al. A time-resolved clonogenic assay for improved cell survival and RBE measurements
US20240309310A1 (en) Plaque detection method and apparatus for imaging of cells
Goodman et al. High-throughput, automated image processing for large-scale fluorescence microscopy experiments
US11893733B2 (en) Treatment efficacy prediction systems and methods
Pan et al. Use of image‐based flow cytometry in bacterial viability analysis using fluorescent probes
Jung et al. CoVET: Cryo-Electron Tomography guided by optical electrophysiology
Lin et al. Evaluation of dynamic cell processes and behavior using video bioinformatics tools
WO2024118582A1 (fr) Plateformes, systèmes et procédés associés pour mesurer la durée de vie et de multiples biomarqueurs moléculaires in vivo du vieillissement
US20240412371A1 (en) Method and apparatus for searching and analyzing cell images

Legal Events

Date Code Title Description
NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 18895741

Country of ref document: EP

Kind code of ref document: A2