scholarly journals Disease-Relevant Single Cell Photonic Signatures Identify S100β Stem Cells and their Myogenic Progeny in Vascular Lesions

2021 ◽  
Author(s):  
Claire Molony ◽  
Damien King ◽  
Mariana Di Luca ◽  
Michael Kitching ◽  
Abidemi Olayinka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stem Cells ◽  
Single Cell ◽  
Ex Vivo ◽  
Lineage Tracing ◽  
Supervised Machine Learning ◽  
Vascular Lesions ◽  
Cell Phenotype ◽  
Genetic Lineage ◽  
Collagen Iii

AbstractA hallmark of subclinical atherosclerosis is the accumulation of vascular smooth muscle cell (SMC)-like cells leading to intimal thickening and lesion formation. While medial SMCs contribute to vascular lesions, the involvement of resident vascular stem cells (vSCs) remains unclear. We evaluated single cell photonics as a discriminator of cell phenotype in vitro before the presence of vSC within vascular lesions was assessed ex vivo using supervised machine learning and further validated using lineage tracing analysis. Using a novel lab-on-a-Disk(Load) platform, label-free single cell photonic emissions from normal and injured vessels ex vivo were interrogated and compared to freshly isolated aortic SMCs, cultured Movas SMCs, macrophages, B-cells, S100β+ mVSc, bone marrow derived mesenchymal stem cells (MSC) and their respective myogenic progeny across five broadband light wavelengths (λ465 - λ670 ± 20 nm). We found that profiles were of sufficient coverage, specificity, and quality to clearly distinguish medial SMCs from different vascular beds (carotid vs aorta), discriminate normal carotid medial SMCs from lesional SMC-like cells ex vivo following flow restriction, and identify SMC differentiation of a series of multipotent stem cells following treatment with transforming growth factor beta 1 (TGF- β1), the Notch ligand Jagged1, and Sonic Hedgehog using multivariate analysis, in part, due to photonic emissions from enhanced collagen III and elastin expression. Supervised machine learning supported genetic lineage tracing analysis of S100β+ vSCs and identified the presence of S100β+vSC-derived myogenic progeny within vascular lesions. We conclude disease-relevant photonic signatures may have predictive value for vascular disease. Graphical abstract

scholarly journals Disease-relevant single cell photonic signatures identify S100β stem cells and their myogenic progeny in vascular lesions

2020 ◽  
Author(s):  
Claire Molony ◽  
Damien King ◽  
Mariana Di Luca ◽  
Abidemi Olayinka ◽  
Roya Hakimjavadi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stem Cells ◽  
Single Cell ◽  
Ex Vivo ◽  
Lineage Tracing ◽  
Supervised Machine Learning ◽  
Vascular Lesions ◽  
Cell Phenotype ◽  
Genetic Lineage ◽  
Collagen Iii

AbstractA hallmark of subclinical atherosclerosis is the accumulation of vascular smooth muscle cell (SMC)-like cells leading to intimal thickening and lesion formation. While medial SMCs contribute to vascular lesions, the involvement of resident vascular stem cells (vSCs) remains unclear. We evaluated single cell photonics as a discriminator of cell phenotype in vitro before the presence of vSC within vascular lesions was assessed ex vivo using supervised machine learning and further validated using lineage tracing analysis. Using a novel lab-on-a-Disk (Load) platform, label-free single cell photonic emissions from normal and injured vessels ex vivo were interrogated and compared to freshly isolated aortic SMCs, cultured Movas SMCs, macrophages, B-cells, S100β+ mVSc, bone marrow derived mesenchymal stem cells (MSC) and their respective myogenic progeny across five broadband light wavelengths (λ465 - λ670 ± 20 nm). We found that profiles were of sufficient coverage, specificity, and quality to clearly distinguish medial SMCs from different vascular beds (carotid vs aorta), discriminate normal carotid medial SMCs from lesional SMC-like cells ex vivo following flow restriction, and identify SMC differentiation of a series of multipotent stem cells following treatment with transforming growth factor beta 1 (TGF-β1), the Notch ligand Jagged1, and Sonic Hedgehog using multivariate analysis, in part, due to photonic emissions from enhanced collagen III and elastin expression. Supervised machine learning supported genetic lineage tracing analysis of S100β+ vSCs and identified the presence of S100β+ vSC-derived myogenic progeny within vascular lesions. We conclude disease-relevant photonic signatures may have predictive value for vascular disease.

scholarly journals A Systematic Evaluation of Methods for Cell Phenotype Classification Using Single-Cell RNA Sequencing Data

2021 ◽  
Author(s):  
Xiaowen Cao ◽  
Li Xing ◽  
Elham Majd ◽  
Hua He ◽  
Junhua Gu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Rna Sequencing ◽  
Simulated Data ◽  
Supervised Machine Learning ◽  
Cell Phenotype ◽  
Data Sets ◽  
Phenotype Classification ◽  
Single Cell Rna Sequencing ◽  
Cell Phenotypes

Abstract Background: Single-cell RNA sequencing (scRNA-seq) yields valuable insights about gene expression and gives critical information about complex tissue cellular composition. In the analysis of single-cell RNA sequencing, the annotations of cell subtypes are often done manually, which is time-consuming and irreproducible. Garnett is a cell-type annotation software based the on elastic net method. Beside cell-type annotation, supervised machine learning methods can also be applied to predict other cell phenotypes from genomic data. Despite the popularity of such applications, there is no existing study to systematically investigate the performance of those supervised algorithms in various sizes of scRNA-seq data sets. Methods and Results: This study evaluates 13 popular supervised machine learning algorithms to classify cell phenotypes, using published real and simulated data sets with diverse cell sizes. The benchmark contained two parts. In the first part, we used real data sets to assess the popular supervised algorithms’ computing speed and cell phenotype classification performance. The classification performances were evaluated using AUC statistics, F1-score, precision, recall, and false-positive rate. In the second part, we evaluated gene selection performance using published simulated data sets with a known list of real genes. Conclusion: The study outcomes showed that ElasticNet with interactions performed best in small and medium data sets. NB was another appropriate method for medium data sets. In large data sets, XGB works excellent. Ensemble algorithms were not significantly superior to individual machine learning methods. Adding interactions to ElasticNet can help, and the improvement was significant in small data sets.

scholarly journals Radiation-induced toxicity in rectal epithelial stem cell contributes to acute radiation injury in rectum

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Felipe Rodriguez Tirado ◽  
Payel Bhanja ◽  
Eduardo Castro-Nallar ◽  
Ximena Diaz Olea ◽  
Catalina Salamanca ◽  
...  
Keyword(s):  
Stem Cells ◽  
Ex Vivo ◽  
Cre Recombinase ◽  
Lineage Tracing ◽  
Common Side Effect ◽  
Rectal Injury ◽  
Male Mice ◽  
Pelvic Irradiation ◽  
Pelvic Malignancies ◽  
Radiation Induced

Abstract Background Radiation-induced rectal epithelial damage is a very common side effect of pelvic radiotherapy and often compromise the life quality and treatment outcome in patients with pelvic malignancies. Unlike small bowel and colon, effect of radiation in rectal stem cells has not been explored extensively. Here we demonstrate that Lgr5-positive rectal stem cells are radiosensitive and organoid-based transplantation of rectal stem cells mitigates radiation damage in rectum. Methods C57Bl6 male mice (JAX) at 24 h were exposed to pelvic irradiation (PIR) to determine the radiation effect in pelvic epithelium. Effect of PIR on Lgr5-positive rectal stem cells (RSCs) was determined in Lgr5-EGFP-Cre-ERT2 mice exposed to PIR. Effect of PIR or clinically relevant fractionated PIR on regenerative response of Lgr5-positive RSCs was examined by lineage tracing assay using Lgr5-eGFP-IRES-CreERT2; Rosa26-CAG-tdTomato mice with tamoxifen administration to activate Cre recombinase and thereby marking the ISC and their respective progeny. Ex vivo three-dimensional organoid cultures were developed from Lgr5-EGFP-Cre-ERT2 mice. Organoid growth was determined by quantifying the budding crypt/total crypt ratio. Organoids from Lgr5-EGFP-ires-CreERT2-TdT mice were transplanted in C57Bl6 male mice exposed to PIR. Engraftment and repopulation of Lgr5-positive RSCs were determined after tamoxifen administration to activate Cre recombinase in recipient mice. Statistical analysis was performed using Log-rank (Mantel-Cox) test and paired two-tail t test. Result Exposure to pelvic irradiation significantly damaged rectal epithelium with the loss of Lgr5+ve rectal stem cells. Radiosensitivity of rectal epithelium was also observed with exposure to clinically relevant fractionated pelvic irradiation. Regenerative capacity of Lgr5+ve rectal stem cells was compromised in response to fractionated pelvic irradiation. Ex vivo organoid study demonstrated that Lgr5+ve rectal stem cells are sensitive to both single and fractionated radiation. Organoid-based transplantation of Lgr5+ve rectal stem cells promotes repair and regeneration of rectal epithelium. Conclusion Lgr5-positive rectal stem cells are radiosensitive and contribute to radiation-induced rectal epithelial toxicity. Transplantation of Lgr5-positive rectal stem cells mitigates radiation-induced rectal injury and promotes repair and regeneration process in rectum.

scholarly journals Identification of leukemic and pre-leukemic stem cells by clonal tracking from single-cell transcriptomics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lars Velten ◽  
Benjamin A. Story ◽  
Pablo Hernández-Malmierca ◽  
Simon Raffel ◽  
Daniel R. Leonce ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Cancer Stem Cells ◽  
Single Cell ◽  
Lineage Tracing ◽  
Specific Gene ◽  
Leukemic Stem Cells ◽  
Hematopoietic Stem ◽  
Mutational Status

AbstractCancer stem cells drive disease progression and relapse in many types of cancer. Despite this, a thorough characterization of these cells remains elusive and with it the ability to eradicate cancer at its source. In acute myeloid leukemia (AML), leukemic stem cells (LSCs) underlie mortality but are difficult to isolate due to their low abundance and high similarity to healthy hematopoietic stem cells (HSCs). Here, we demonstrate that LSCs, HSCs, and pre-leukemic stem cells can be identified and molecularly profiled by combining single-cell transcriptomics with lineage tracing using both nuclear and mitochondrial somatic variants. While mutational status discriminates between healthy and cancerous cells, gene expression distinguishes stem cells and progenitor cell populations. Our approach enables the identification of LSC-specific gene expression programs and the characterization of differentiation blocks induced by leukemic mutations. Taken together, we demonstrate the power of single-cell multi-omic approaches in characterizing cancer stem cells.

scholarly journals Hlf Expression Marks Early Emergence of Hematopoietic Stem Cell Precursors With Adult Repopulating Potential and Fate

2021 ◽  
Vol 9 ◽  
Author(s):  
Wanbo Tang ◽  
Jian He ◽  
Tao Huang ◽  
Zhijie Bai ◽  
Chaojie Wang ◽  
...  
Keyword(s):  
Stem Cells ◽  
Mouse Model ◽  
Hematopoietic Stem Cells ◽  
Fetal Liver ◽  
Lineage Tracing ◽  
Genetic Lineage ◽  
Hematopoietic Stem ◽  
Genetic Lineage Tracing

In the aorta-gonad-mesonephros (AGM) region of mouse embryos, pre-hematopoietic stem cells (pre-HSCs) are generated from rare and specialized hemogenic endothelial cells (HECs) via endothelial-to-hematopoietic transition, followed by maturation into bona fide hematopoietic stem cells (HSCs). As HECs also generate a lot of hematopoietic progenitors not fated to HSCs, powerful tools that are pre-HSC/HSC-specific become urgently critical. Here, using the gene knockin strategy, we firstly developed an Hlf-tdTomato reporter mouse model and detected Hlf-tdTomato expression exclusively in the hematopoietic cells including part of the immunophenotypic CD45– and CD45+ pre-HSCs in the embryonic day (E) 10.5 AGM region. By in vitro co-culture together with long-term transplantation assay stringent for HSC precursor identification, we further revealed that unlike the CD45– counterpart in which both Hlf-tdTomato-positive and negative sub-populations harbored HSC competence, the CD45+ E10.5 pre-HSCs existed exclusively in Hlf-tdTomato-positive cells. The result indicates that the cells should gain the expression of Hlf prior to or together with CD45 to give rise to functional HSCs. Furthermore, we constructed a novel Hlf-CreER mouse model and performed time-restricted genetic lineage tracing by a single dose induction at E9.5. We observed the labeling in E11.5 AGM precursors and their contribution to the immunophenotypic HSCs in fetal liver (FL). Importantly, these Hlf-labeled early cells contributed to and retained the size of the HSC pool in the bone marrow (BM), which continuously differentiated to maintain a balanced and long-term multi-lineage hematopoiesis in the adult. Therefore, we provided another valuable mouse model to specifically trace the fate of emerging HSCs during development.

scholarly journals Infinity Flow: High-throughput single-cell quantification of 100s of proteins using conventional flow cytometry and machine learning

2020 ◽  
Author(s):  
Etienne Becht ◽  
Daniel Tolstrup ◽  
Charles-Antoine Dutertre ◽  
Florent Ginhoux ◽  
Evan W. Newell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Single Cell ◽  
Low Cost ◽  
Expression Patterns ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Supervised Machine Learning ◽  
Melanoma Metastasis ◽  
Immunologic Research

AbstractModern immunologic research increasingly requires high-dimensional analyses in order to understand the complex milieu of cell-types that comprise the tissue microenvironments of disease. To achieve this, we developed Infinity Flow combining hundreds of overlapping flow cytometry panels using machine learning to enable the simultaneous analysis of the co-expression patterns of 100s of surface-expressed proteins across millions of individual cells. In this study, we demonstrate that this approach allows the comprehensive analysis of the cellular constituency of the steady-state murine lung and to identify novel cellular heterogeneity in the lungs of melanoma metastasis bearing mice. We show that by using supervised machine learning, Infinity Flow enhances the accuracy and depth of clustering or dimensionality reduction algorithms. Infinity Flow is a highly scalable, low-cost and accessible solution to single cell proteomics in complex tissues.

scholarly journals Repopulating Kupffer Cells Originate Directly from Hematopoietic Stem Cells

2021 ◽  
Author(s):  
Xu Fan ◽  
Pei Lu ◽  
Xianghua Cui ◽  
Peng Wu ◽  
Weiran Lin ◽  
...  
Keyword(s):  
Stem Cells ◽  
Hematopoietic Stem Cells ◽  
Kupffer Cells ◽  
Lineage Tracing ◽  
Genetic Lineage ◽  
Fate Mapping ◽  
Monocytic Cell ◽  
Hematopoietic Stem ◽  
Cellular Origin

Abstract Kupffer cells (KCs) originate from yolk sac progenitors before birth, but the origin of repopulating KCs in adult remains unclear. In current study, we firstly traced the fate of preexisting KCs and that of monocytic cells with tissue-resident macrophage-specific and monocytic cell-specific fate mapping mouse models, respectively, and found no evidences that repopulating KCs originate from preexisting KCs or MOs. Secondly, we performed genetic lineage tracing to determine the type of progenitor cells involved in response to KC depletion in mice, and found that in response to KC depletion, hematopoietic stem cells (HSCs) proliferated in the bone marrow, mobilized into the blood, adoptively transferred into the liver and differentiated into KCs. Finally, we traced the fate of HSCs in a HSC-specific fate-mapping mouse model, in context of chronic liver inflammation induced by repeated carbon tetrachloride treatment, and confirmed that repopulating KCs originated directly from HSCs. Taken together, these findings provided in vivo fate-mapping evidences that repopulating KCs originate directly from hematopoietic stem cells, which present a completely novel understanding of the cellular origin of repopulating Kupffer Cells and shedding light on the divergent roles of KCs in liver homeostasis and diseases.

scholarly journals Perivascular-Derived Mesenchymal Stem Cells

2019 ◽  
Vol 98 (10) ◽  
pp. 1066-1072 ◽  
Author(s):  
V. Yianni ◽  
P.T. Sharpe
Keyword(s):  
Tissue Engineering ◽  
Stem Cells ◽  
Mesenchymal Stem Cells ◽  
Dental Pulp ◽  
Molecular Mechanisms ◽  
Lineage Tracing ◽  
Specific Gene ◽  
Genetic Lineage ◽  
Differentiation Potential

Cells have been identified in postnatal tissues that, when isolated from multiple mesenchymal compartments, can be stimulated in vitro to give rise to cells that resemble mature mesenchymal phenotypes, such as odontoblasts, osteoblasts, adipocytes, and myoblasts. This has made these adult cells, collectively called mesenchymal stem cells (MSCs), strong candidates for fields such as tissue engineering and regenerative medicine. Based on evidence from in vivo genetic lineage–tracing studies, pericytes have been identified as a source of MSC precursors in vivo in multiple organs, in response to injury or during homeostasis. Questions of intense debate and interest in the field of tissue engineering and regenerative studies include the following: 1) Are all pericytes, irrespective of tissue of isolation, equal in their differentiation potential? 2) What are the mechanisms that regulate the differentiation of MSCs? To gain a better understanding of the latter, recent work has utilized ChIP-seq (chromatin immunoprecipitation followed by sequencing) to reconstruct histone landscapes. This indicated that for dental pulp pericytes, the odontoblast-specific gene Dspp was found in a transcriptionally permissive state, while in bone marrow pericytes, the osteoblast-specific gene Runx2 was primed for expression. RNA sequencing has also been utilized to further characterize the 2 pericyte populations, and results highlighted that dental pulp pericytes are already precommitted to an odontoblast fate based on enrichment analysis indicating overrepresentation of key odontogenic genes. Furthermore, ChIP-seq analysis of the polycomb repressive complex 1 component RING1B indicated that this complex is likely to be involved in inhibiting inappropriate differentiation, as it localized to a number of loci of key transcription factors that are needed for the induction of adipogenesis, chondrogenesis, or myogenesis. In this review, we highlight recent data elucidating molecular mechanisms that indicate that pericytes can be tissue-specific precommitted MSC precursors in vivo and that this precommitment is a major driving force behind MSC differentiation.

scholarly journals Troy+ brain stem cells cycle through quiescence and regulate their number by sensing niche occupancy

2018 ◽  
Vol 115 (4) ◽  
pp. E610-E619 ◽  
Author(s):  
Onur Basak ◽  
Teresa G. Krieger ◽  
Mauro J. Muraro ◽  
Kay Wiebrands ◽  
Daniel E. Stange ◽  
...  
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Cell Number ◽  
Lineage Tracing ◽  
Rapid Expansion ◽  
Molecular Signature ◽  
Genetic Lineage ◽  
Proliferating Cells ◽  
Self Renewal ◽  
Genetic Lineage Tracing

The adult mouse subependymal zone provides a niche for mammalian neural stem cells (NSCs). However, the molecular signature, self-renewal potential, and fate behavior of NSCs remain poorly defined. Here we propose a model in which the fate of active NSCs is coupled to the total number of neighboring NSCs in a shared niche. Using knock-in reporter alleles and single-cell RNA sequencing, we show that the Wnt target Tnfrsf19/Troy identifies both active and quiescent NSCs. Quantitative analysis of genetic lineage tracing of individual NSCs under homeostasis or in response to injury reveals rapid expansion of stem-cell number before some return to quiescence. This behavior is best explained by stochastic fate decisions, where stem-cell number within a shared niche fluctuates over time. Fate mapping proliferating cells using a Ki67iresCreER allele confirms that active NSCs reversibly return to quiescence, achieving long-term self-renewal. Our findings suggest a niche-based mechanism for the regulation of NSC fate and number.

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