Isolation and characterization of individual functionally reactive cytotoxic T lymphocytes: conjugation, killing and recycling at the single cell level

Mesenchymal stem/stromal cells (MSCs) are promising cell sources for regenerative medicine and the treatment of autoimmune disorders. Comparing MSCs from different tissues at the single-cell level is fundamental for optimizing clinical applications. Here we analyzed single-cell RNA-seq data of MSCs from four tissues, namely umbilical cord, bone marrow, synovial tissue, and adipose tissue. We identified three major cell subpopulations, namely osteo-MSCs, chondro-MSCs, and adipo/myo-MSCs, across all MSC samples. MSCs from the umbilical cord exhibited the highest immunosuppression, potentially indicating it is the best immune modulator for autoimmune diseases. MSC subpopulations, with different subtypes and tissue sources, showed pronounced differences in differentiation potentials. After we compared the cell subpopulations and cell status pre-and-post chondrogenesis induction, osteogenesis induction, and adipogenesis induction, respectively, we found MSC subpopulations expanded and differentiated when their subtypes consist with induction directions, while the other subpopulations shrank. We identified the genes and transcription factors underlying each induction at the single-cell level and subpopulation level, providing better targets for improving induction efficiency.

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Single-cell RNA-Seq reveals the link between CD45 isoforms and tumor-infiltrating T cells heterogeneity in liver cancer

10.1101/2020.03.22.002824 ◽

2020 ◽

Author(s):

Biaofeng Zhou ◽

Shang Liu ◽

Liang Wu ◽

Yan Sun ◽

Jie Chen ◽

...

Keyword(s):

T Cells ◽

T Cell ◽

T Lymphocytes ◽

Single Cell ◽

Cell Function ◽

Effector Memory ◽

Single Cell Level ◽

Cell Level ◽

Cd45 Isoforms ◽

And Function

AbstractCD45 isoforms play a major role in characterizing T cell function, phenotype, and development. However, there is lacking comprehensive interrogation about the relationship between CD45 isoforms and T lymphocytes from cancer patients at the single-cell level yet. Here, we investigated the CD45 isoforms component of published 5,063 T cells of hepatocellular carcinoma (HCC), which has been assigned functional states. We found that the distribution of CD45 isoforms in T lymphocytes cells depended on tissue resource, cell type, and functional state. Further, we demonstrated that CD45RO and CD45RA dominate in characterizing the phenotype and function of T cell though multiple CD45 isoforms coexist in T cells, through a novel alternative splicing pattern analysis. We identified a novel development trajectory of tumor-infiltrating T cells from Tcm to Temra (effector memory T cells re-expresses CD45RA) after detecting two subpopulations in state of transition, Tcm (central memory T) and Tem (effector memory T). Temra, capable of high cytotoxic characteristics, was discovered to be associated with the stage of HCC and may be a target of immunotherapy. Our study presents a comprehension of the connection between CD45 isoforms and the function, states, sources of T lymphocytes cells in HCC patients at the single-cell level, providing novel insight for the effect of CD45 isoforms on T cell heterogeneity.

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Quantitative analysis “at the single cell level” of the novel CD28-CD11b-subpopulation of CD8+ T lymphocytes

Journal of Dermatological Science ◽

10.1016/s0923-1811(98)83564-0 ◽

1998 ◽

Vol 16 ◽

pp. S95 ◽

Cited By ~ 3

Author(s):

A. Lonati ◽

S. Licenziati ◽

M. Marcelli ◽

D. Canaris ◽

G. Pasolini ◽

...

Keyword(s):

Quantitative Analysis ◽

T Lymphocytes ◽

Single Cell ◽

Single Cell Level ◽

The Novel ◽

Cell Level ◽

Cd8 T Lymphocytes

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Phenotypic Characterization of Antibiotic Persisters at the Single-Cell Level: From Data Acquisition to Data Analysis

10.1007/978-1-0716-1621-5_7 ◽

2021 ◽

pp. 95-106

Author(s):

Nathan Fraikin ◽

Laurence Van Melderen ◽

Frédéric Goormaghtigh

Keyword(s):

Data Analysis ◽

Data Acquisition ◽

Single Cell ◽

Phenotypic Characterization ◽

Single Cell Level ◽

Cell Level

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Application of Imaging Flow Cytometry for the Characterization of Intracellular Attributes in Chinese Hamster Ovary Cell Lines at the Single‐Cell Level

Biotechnology Journal ◽

10.1002/biot.201800675 ◽

2019 ◽

Vol 14 (7) ◽

pp. 1800675 ◽

Cited By ~ 1

Author(s):

Eva Pekle ◽

Andrew Smith ◽

Guglielmo Rosignoli ◽

Christopher Sellick ◽

C. M. Smales ◽

...

Keyword(s):

Flow Cytometry ◽

Single Cell ◽

Cell Lines ◽

Chinese Hamster Ovary ◽

Chinese Hamster ◽

Ovary Cell ◽

Single Cell Level ◽

Cell Level ◽

Imaging Flow Cytometry

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Surface-enhanced Raman spectroscopy of microorganisms: limitations and applicability on the single-cell level

The Analyst ◽

10.1039/c8an02177e ◽

2019 ◽

Vol 144 (3) ◽

pp. 943-953 ◽

Cited By ~ 12

Author(s):

Ruben Weiss ◽

Márton Palatinszky ◽

Michael Wagner ◽

Reinhard Niessner ◽

Martin Elsner ◽

...

Keyword(s):

Raman Spectroscopy ◽

Single Cell ◽

Environmental Studies ◽

Surface Enhanced Raman Spectroscopy ◽

Clinical Diagnostics ◽

Single Cell Level ◽

Cell Level ◽

Surface Enhanced ◽

Surface Enhanced Raman

Detection and characterization of microorganisms is essential for both clinical diagnostics and environmental studies.

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The Isolation and Characterization of a Family of Serine Protease Genes Expressed in Activated Cytotoxic T Lymphocytes

Immunological Reviews ◽

10.1111/j.1600-065x.1988.tb00746.x ◽

1988 ◽

Vol 103 (1) ◽

pp. 5-19 ◽

Cited By ~ 65

Author(s):

R. Chris Bleackley ◽

Corrinne G. Lobe ◽

Brenda Duggan. ◽

Nancy Ehrman ◽

Chantal Fregeau ◽

...

Keyword(s):

T Lymphocytes ◽

Serine Protease ◽

Cytotoxic T Lymphocytes ◽

Isolation And Characterization

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Identifying tumor cells at the single cell level

10.1101/2021.10.15.463909 ◽

2021 ◽

Author(s):

Jan Dohmen ◽

Artem Baranovskii ◽

Bora Uyar ◽

Jonathan Ronen ◽

Vedran Franke ◽

...

Keyword(s):

Single Cell ◽

Tumor Cells ◽

Biomarker Discovery ◽

Single Cell Analysis ◽

High Sensitivity ◽

Simple Problem ◽

Cancer Therapies ◽

Single Cell Level ◽

Cell Level

Tumors are highly complex tissues composed of cancerous cells, surrounded by a heterogeneous cellular microenvironment. Tumor response to treatments is governed by an interaction of cancer cell intrinsic factors with external influences of the tumor microenvironment. Disentangling the heterogeneity within a tumor is a crucial step in developing and utilization of effective cancer therapies. Single cell sequencing has the potential to revolutionize personalized medicine. In cancer therapy it enables an effective characterization of the complete heterogeneity within the tumor. A governing challenge in cancer single cell analysis is cell annotation, the assignment of a particular cell type or a cell state to each sequenced cell. We propose Ikarus, a machine learning pipeline aimed at solving a perceived simple problem, distinguishing tumor cells from normal cells at the single cell level. Automatic characterization of tumor cells is a critical limiting step for a multitude of research, clinical, and commercial applications. Automatic characterization of tumor cells would expedite neoantigen prediction, automatic characterization of tumor cell states, it would greatly facilitate cancer biomarker discovery. Such a tool can be used for automatic annotation of histopathological data, profiled using multichannel immunofluorescence or spatial sequencing. We have tested ikarus on multiple single cell datasets to ascertain that it achieves high sensitivity and specificity in multiple experimental contexts.

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