Intelligent image-activated cell sorting 2.0

Isozaki, Akihiro; Mikami, Hideharu; Tezuka, Hiroshi; Matsumura, Hirotoshi; Huang, Kangrui; Akamine, Marino; Hiramatsu, Kazumasa; Iino, Takanori; Ito, Takuro; Karakawa, Hiroshi; Kasai, Yusuke; Li, Yan; Nakagawa, Yuta; Ohnuki, Shinsuke; Ota, Tadataka; Qian, Yong; Sakuma, Shinya; Sekiya, Takeichiro; Shirasaki, Yoshitaka; Suzuki, Nobutake; Tayyabi, Ehsen; Wakamiya, Tsubasa; Xu, Muzhen; Yamagishi, Mai; Yan, Haochen; Qiang, Yu; Yan, Sheng; Yuan, Dan; Zhang, Wei; Zhao, Yaqi; Arai, Fumihito; Campbell, Robert E.; Danelon, Christophe; Carlo, Dino Di; Hiraki, Kei; Hoshino, Yu; Hosokawa, Yoichiroh; Inaba, Masaaki; Nakagawa, Atsuhiro; Ohya, Yoshikazu; Oikawa, Minoru; Uemura, Shunsuke; Ozeki, Yasuyuki; Sugimura, Takeshi; Nitta, Nao; Goda, Keisuke

doi:10.1039/d0lc00080a

Cited by 109 publications

(96 citation statements)

References 38 publications

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“…Similarly, deterministic encapsulation could be triggered by artificial intelligence consequently to object recognition. 142,143 The capacity of elaborating large amount of data together with intrinsic predictive functionalities, makes artificial intelligence among the most suitable candidates to face the challenges associated to the derivation of a general framework for the production of microfluidic crystals.…”

Section: Discussionmentioning

confidence: 99%

Microfluidic formation of crystal-like structures

2021

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Section: Discussionmentioning

confidence: 99%

Microfluidic formation of crystal-like structures

2021

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“…For now, we would not necessarily advise comparing edge strengths between the networks (e.g., between Figures 1, 2, 3 in this study) unless the BN-generating datasets are very similar in their size and structure. Instead, when possible, the data should be pooled, and indicator/contrast variables (distinguishing the networks to be compared) introduced (e.g., Figures 5,6 in this study).…”

Section: Discussionmentioning

confidence: 99%

“…Automated analysis of high-dimensional cytometry data is an active bioinformatics research area [1,2]. Increasingly more sophisticated approaches, mostly of the data mining / machine learning variety, have recently been proposed for the primary analysis of such data [3][4][5][6][7]. However, emphasis remains on primary data analysis, which is largely reduced to automated gating, clustering, visualization and, subsequently and optionally, cellular identity / population type assignment [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data

Rodin

Gogoshin

Wang

et al. 2020

Preprint

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Cancer immunotherapy, specifically immune checkpoint blockade therapy, has been found to be effective in the treatment of metastatic cancers. However, only a subset of patients with certain cancer types achieve clinical responses. Consequently, elucidating immune system-related pre-treatment biomarkers that are predictive with respect to sustained clinical response is a major research priority. Another research priority is evaluating changes in the immune system before and after treatment in responders and non-responders. Specifically, our group has been studying immune signaling networks as an accurate reflection of the global immune state. Flow cytometry data (FACS, Fluorescence-activated cell sorting) characterizing immune signaling in peripheral blood mononuclear cells (PBMC) from gastroesophageal adenocarcinoma (GEA) patients were used to analyze changes in immune signaling networks in this setting. We developed a novel computational pipeline to perform secondary analyses of FACS data using systems biology / machine learning / information-theoretic techniques and concepts, primarily based on Bayesian network modeling. Application of this novel pipeline resulted in determination of immune markers, combinations / interactions thereof, and corresponding immune cell population types that are associated with clinical responses. Future studies are planned to generalize our analytical approach to different cancer types and corresponding datasets. 6 Author SummaryIt is difficult to predict whether a cancer patient undergoing immunotherapy treatments will respond. As immunotherapy is expensive and may lead to autoimmune toxicities, patient selection is an important issue. One way to gain deeper insight into the underlying processes is to study changes in immune signaling networks during the treatment course. These networks can be modeled, visualized, and quantified using systems biology / machine learning methods, such as Bayesian networks (BNs). Here, we present a BN-based analytical strategy for devising and comparing immune signaling networks, and apply it to data obtained from patients in a gastrointestinal cancer immunotherapy clinical trial. We identify potentially predictive immune biomarkers, and compare and contrast the resulting network models in different groups of patients, before and after therapy. Our analytical strategy generalizes to different cancers and immunotherapy regimens.

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“…Cells' (and by extension, vesicles') "physiological" functions often manifest in morphological identities and molecular localization footprints, and therefore an imaging-based screening approach would offer an unprecedented advantage over conventional high-throughput screening methods, such as FACS. One emerging technology that would enable enrichment based on multidimensional fluorescence images is intelligent image-activated cell sorting (IACS; Figure 2H; Nitta et al, 2018;Isozaki et al, 2020). Imageactivated cell sorting integrates microfluidics, high-throughput cell microscopy, real-time intelligent image processing, decision making, and cell sorting.…”

Section: Selection/screening Strategiesmentioning

confidence: 99%

Roadmap to Building a Cell: An Evolutionary Approach

Abil

Danelon

2020

Front. Bioeng. Biotechnol.

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Laboratory synthesis of an elementary biological cell from isolated components may aid in understanding of the fundamental principles of life and will provide a platform for a range of bioengineering and medical applications. In essence, building a cell consists in the integration of cellular modules into system's level functionalities satisfying a definition of life. To achieve this goal, we propose in this perspective to undertake a semirational, system's level evolutionary approach. The strategy would require iterative cycles of genetic integration of functional modules, diversification of hereditary information, compartmentalized gene expression, selection/screening, and possibly, assistance from open-ended evolution. We explore the underlying challenges to each of these steps and discuss possible solutions toward the bottom-up construction of an artificial living cell.

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Intelligent image-activated cell sorting 2.0

Abstract: The upgraded version of intelligent image-activated cell sorting (iIACS) has enabled higher-throughput and more sensitive intelligent image-based sorting of single live cells from heterogeneous populations.

Cited by 109 publications

References 38 publications

Microfluidic formation of crystal-like structures

Microfluidic formation of crystal-like structures

Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data

Roadmap to Building a Cell: An Evolutionary Approach

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