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ThinkCyte, Inc.: Machine vision-based cell sorting ...
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ADVERTISEMENT FEATURE ADVERTISER RETAINS SOLE RESPONSIBILITY FOR CONTENT B48 | June 2021 | www.nature.com/biopharmdeal ThinkCyte, Inc. www.thinkcyte.com ThinkCyte, Inc.: Machine vision-based cell sorting transforms cell therapy and drug discovery Combining cell sorting technology with artificial intelligence, Japan-based ThinkCyte aims to discover novel high- value therapeutic cells, new functional genes, and novel drug candidates for itself and its partners worldwide. It is often said that a picture is worth a thou- sand words, but half a century since the first fluorescence-activated cell sorting (FACS) was launched, researchers still do not have a commer- cial tool to sort single cells at high speed based on image information. Overcoming the technological challenges, ThinkCyte has developed Ghost Cytometry, a proprietary machine Vision-based Cell Sorting (ViCS) technology that integrates a novel ultrafast imaging technique with artificial intelligence (AI) to identify and sort cells based on image informa- tion at record high-throughput rates—even above 3,000 cells per second (Fig. 1). In contrast to the single spotlight used in conventional FACS, ViCS involves obtaining cellular image information from the motion of cells relative to a static patterned optical structure, and thereby compressively converting it into temporal waveforms. Applying machine-learning methods directly to the com- pressed waveforms—without time-consuming image reconstruction—enables real-time ‘image- free’ vision-based cell sorting 1 . Broad applications This high-throughput vision-based classification and isolation of cells has broad potential applica- tions, ranging from cell therapy and regenerative medicine to drug discovery and diagnostics. A key focus is cell therapy, as ViCS enables the detection and isolation of target cells without using cell surface markers or stains. Cells can be classi- fied and isolated according to not only apparent morphological differences (such as those between live and dead cells, activated and resting T cells, and differentiated and undifferentiated stem cells) but also more subtle characteristic morphological variance in cells and organelles (such as cell-cycle phases, glycolytic levels, and apoptotic status). Unwanted cells and particulates, including cancer- ous cells, monocytes, and plastic beads, can also be monitored and removed without using markers. In 2020, ThinkCyte and Hitachi, Ltd. jointly announced a collaboration to develop an AI-driven cell analysis and sorting system using ViCS for the use in cell manufacturing processes. “The unprecedented speed of our ViCS technol- ogy offers significant advantages over cell surface marker-based selection methods currently preva- lent in cell therapy,” explained Hikaru Nagahori, CBO at ThinkCyte. “The high-throughput ability to characterize and isolate cell populations based on their image information without the use of markers or stains is highly compatible with cell manufacturing automation and enables stable, affordable, large-scale, high-quality standardized cell manufacturing processes.” Another area that ThinkCyte’s ViCS is set to impact significantly is high-content screening of genes and drugs. Conventionally, a pile of microplates and a microscope have been used for high-content screening to identify functional genes and drug candidates that alter cell image pheno- types including changes in protein localizations, aggregation, cell morphology, and intracellular organelles. Although powerful, this arrayed format method is a relatively time-consuming and costly process and is not suitable for suspended cells. Using ViCS, however, ThinkCyte has developed a workflow to perform such high-content screens in a pooled format (starting from a suspension of pooled cells in a tube). “Our screening platform significantly acceler- ates the screening processes and is applicable to a variety of libraries, including CRISPR/Cas9 sgRNA, shRNA, and even small compounds,” said Asako Tsubouchi, head of Drug Discovery at ThinkCyte. “Furthermore, label-free distinction of disease- associated cells that have subtle morphological differences provides a new way of linking genotype to phenotype and will offer unique phenotypic endpoints, including those which are often difficult to distinguish using the human eye.” Partnering goals ThinkCyte has already partnered with multiple lead- ing global pharmaceutical companies and research institutes to utilize ViCS in life science and medical fields. ThinkCyte is seeking new partners interested in conducting collaborative research using its plat- form to discover novel high-value therapeutic cells, new functional genes, and novel drug candidates and/or collaborating on instrument development to customize its cell analysis and sorting system to suit partners’ cell-manufacturing processes. “Our unique ViCS is a high-throughput and high content cell sorting system that has the potential to revolutionize cell therapies and drug discovery to benefit patients and their families worldwide,” said Waichiro Katsuda, ThinkCyte’s co-founder and CEO. “We are confident that our unique and innovative technology will solve key issues in the biopharmaceutical industry and look forward to establishing new partnerships to make a better world together.” 1. Ota, S. et al. Science 360, 1246–1251 (2018). Fig. 1 | Machine vision-based cell sorting (ViCS) technology overview. (a) Schematic of the ViCS. (i) Image information of each cell is first compressively converted into a temporal waveform as the cell passes through a light illumination pattern projected onto a microchannel. This waveform is then recorded by using a single pixel detector. (ii) A trained AI model is applied directly to the waveforms for predicting the cell types. (iii) The cells classified based on the machine prediction are gently isolated by fluid pressure. (b) Examples of phenotypes which ViCS can distinguish in fluorescence and label-free modes. All images were taken by Amnis FlowSight. Hikaru Nagahori, CBO ThinkCyte, Inc. San Carlos, CA, USA Email: [email protected] CONTACT
Transcript of ThinkCyte, Inc.: Machine vision-based cell sorting ...
A D V E R T I S E M E N T F E A T U R E
A D V E R T I S E R R E TA I N S S O L E R E S P O N S I B I L I T Y F O R C O N T E N TB48 | June 2021 | www.nature.com/biopharmdeal
ThinkCyte, Inc.www.thinkcyte.com
ThinkCyte, Inc.: Machine vision-based cell sortingtransforms cell therapy and drug discoveryCombining cell sorting technology with artificial intelligence, Japan-based ThinkCyte aims to discover novel high-value therapeutic cells, new functional genes, and novel drug candidates for itself and its partners worldwide.
It is often said that a picture is worth a thou-sand words, but half a century since the firstfluorescence-activated cell sorting (FACS) waslaunched, researchers still do not have a commer-cial tool to sort single cells at high speed basedon image information.
Overcoming the technological challenges,ThinkCyte has developed Ghost Cytometry, aproprietary machine Vision-based Cell Sorting(ViCS) technology that integrates a novel ultrafastimaging technique with artificial intelligence (AI)to identify and sort cells based on image informa-tion at record high-throughput rates—even above3,000 cells per second (Fig. 1). In contrast to thesingle spotlight used in conventional FACS, ViCSinvolves obtaining cellular image information fromthe motion of cells relative to a static patternedoptical structure, and thereby compressivelyconverting it into temporal waveforms. Applyingmachine-learning methods directly to the com-pressed waveforms—without time-consumingimage reconstruction—enables real-time ‘image-free’ vision-based cell sorting1.
Broad applicationsThis high-throughput vision-based classificationand isolation of cells has broad potential applica-tions, ranging from cell therapy and regenerativemedicine to drug discovery and diagnostics.
A key focus is cell therapy, as ViCS enables thedetection and isolation of target cells without usingcell surface markers or stains. Cells can be classi-fied and isolated according to not only apparentmorphological differences (such as those betweenlive and dead cells, activated and resting T cells,and differentiated and undifferentiated stem cells)but also more subtle characteristic morphologicalvariance in cells and organelles (such as cell-cyclephases, glycolytic levels, and apoptotic status).Unwanted cells and particulates, including cancer-ous cells, monocytes, and plastic beads, can also bemonitored and removed without using markers. In2020, ThinkCyte and Hitachi, Ltd. jointly announceda collaboration to develop an AI-driven cell analysisand sorting system using ViCS for the use in cellmanufacturing processes.
“The unprecedented speed of our ViCS technol-ogy offers significant advantages over cell surfacemarker-based selection methods currently preva-lent in cell therapy,” explained Hikaru Nagahori,CBO at ThinkCyte. “The high-throughput abilityto characterize and isolate cell populations basedon their image information without the use ofmarkers or stains is highly compatible with cell
manufacturing automation and enables stable,affordable, large-scale, high-quality standardizedcell manufacturing processes.”
Another area that ThinkCyte’s ViCS is set toimpact significantly is high-content screeningof genes and drugs. Conventionally, a pile ofmicroplates and a microscope have been used forhigh-content screening to identify functional genesand drug candidates that alter cell image pheno-types including changes in protein localizations,aggregation, cell morphology, and intracellularorganelles. Although powerful, this arrayed formatmethod is a relatively time-consuming and costlyprocess and is not suitable for suspended cells.Using ViCS, however, ThinkCyte has developed aworkflow to perform such high-content screensin a pooled format (starting from a suspension ofpooled cells in a tube).
“Our screening platform significantly acceler-ates the screening processes and is applicable to avariety of libraries, including CRISPR/Cas9 sgRNA,shRNA, and even small compounds,” said AsakoTsubouchi, head of Drug Discovery at ThinkCyte.“Furthermore, label-free distinction of disease-associated cells that have subtle morphologicaldifferences provides a new way of linking genotypeto phenotype and will offer unique phenotypicendpoints, including those which are often difficultto distinguish using the human eye.”
Partnering goalsThinkCyte has already partnered with multiple lead-ing global pharmaceutical companies and researchinstitutes to utilize ViCS in life science and medicalfields. ThinkCyte is seeking new partners interestedin conducting collaborative research using its plat-form to discover novel high-value therapeutic cells,new functional genes, and novel drug candidatesand/or collaborating on instrument developmentto customize its cell analysis and sorting system tosuit partners’ cell-manufacturing processes.
“Our unique ViCS is a high-throughput and highcontent cell sorting system that has the potentialto revolutionize cell therapies and drug discoveryto benefit patients and their families worldwide,”said Waichiro Katsuda, ThinkCyte’s co-founderand CEO. “We are confident that our unique andinnovative technology will solve key issues in thebiopharmaceutical industry and look forward toestablishing new partnerships to make a betterworld together.”1. Ota, S. et al. Science 360, 1246–1251 (2018).
Fig. 1 | Machine vision-based cell sorting (ViCS) technology overview. (a) Schematic of the ViCS.(i) Image information of each cell is first compressively converted into a temporal waveform as the cellpasses through a light illumination pattern projected onto a microchannel. This waveform is thenrecorded by using a single pixel detector. (ii) A trained AI model is applied directly to the waveforms forpredicting the cell types. (iii) The cells classified based on the machine prediction are gently isolated byfluid pressure. (b) Examples of phenotypes which ViCS can distinguish in fluorescence and label-freemodes. All images were taken by Amnis FlowSight.
Hikaru Nagahori, CBOThinkCyte, Inc.San Carlos, CA, USAEmail: [email protected]
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