Spectrophotometric analysis at the single-cell level: elucidating dispersity within melanic immortalized cell populations

The Analyst ◽  
2017 ◽  
Vol 142 (9) ◽  
pp. 1482-1491 ◽  
Author(s):  
Luis Polo-Parada ◽  
Gerardo Gutiérrez-Juárez ◽  
David Cywiak ◽  
Rafael Pérez-Solano ◽  
Gary A. Baker
Keyword(s):  
Single Cell ◽  
Spectrophotometric Analysis ◽  
Optical Characteristics ◽  
Cell Populations ◽  
Single Cell Level ◽  
Cell Level ◽  
Immortalized Cell

The widely held notion that melanin-containing cells are uniform in both size and optical characteristics is demonstrably false.

scholarly journals Identification of two distinct pathways of human myelopoiesis

2019 ◽  
Vol 4 (35) ◽  
pp. eaau7148 ◽  
Author(s):  
Roy Drissen ◽  
Supat Thongjuea ◽  
Kim Theilgaard-Mönch ◽  
Claus Nerlov
Keyword(s):  
Mast Cell ◽  
Single Cell ◽  
Lineage Commitment ◽  
Cell Populations ◽  
Single Cell Level ◽  
Cell Level ◽  
Lymphoid Lineage ◽  
Myeloid Progenitor ◽  
Multipotent Cells ◽  
Cellular Pathways

Human myelopoiesis has been proposed to occur through oligopotent common myeloid progenitor (CMP) and lymphoid-primed multipotent progenitor (LMPP) populations. However, other studies have proposed direct commitment of multipotent cells to unilineage fates, without specific intermediary lineage cosegregation patterns. We here show that distinct human myeloid progenitor populations generate the neutrophil/monocyte and mast cell/basophil/eosinophil lineages as previously shown in mouse. Moreover, we find that neutrophil/monocyte potential selectively cosegregates with lymphoid lineage and mast cell/basophil/eosinophil potentials with megakaryocyte/erythroid potential early during lineage commitment. Furthermore, after this initial commitment step, mast cell/basophil/eosinophil and megakaryocyte/erythroid potentials colocalize at the single-cell level in restricted oligopotent progenitors. These results show that human myeloid lineages are generated through two distinct cellular pathways defined by complementary oligopotent cell populations.

scholarly journals Synovial single-cell heterogeneity, zonation, and interactions: a patchwork of effectors in arthritis

Rheumatology ◽  
2021 ◽  
Author(s):  
Barbora Schonfeldova ◽  
Kristina Zec ◽  
Irina A Udalova
Keyword(s):  
Rheumatoid Arthritis ◽  
Single Cell ◽  
Cell Signalling ◽  
Synovial Cell ◽  
Cellular Heterogeneity ◽  
Cell Populations ◽  
Single Cell Level ◽  
Cell Heterogeneity ◽  
Cell Level ◽  
Peripheral Neurons

Abstract Despite extensive research, there is still no treatment that would lead to remission in all patients with rheumatoid arthritis as our understanding of the affected site, the synovium, is still incomplete. Recently, single-cell technologies helped to decipher the cellular heterogeneity of the synovium; however, certain synovial cell populations, such as endothelial cells or peripheral neurons, remain to be profiled on a single-cell level. Furthermore, associations between certain cellular states and inflammation were found; whether these cells cause the inflammation remains to be answered. Similarly, cellular zonation and interactions between individual effectors in the synovium are yet to be fully determined. A deeper understanding of cell signalling and interactions in the synovium is crucial for a better design of therapeutics with the goal of complete remission in all patients.

Adoptively transferred Th1 cell populations lose IFNγ+ cells by cytokine down-regulation on single-cell level

2006 ◽  
Vol 107 (2) ◽  
pp. 176-181 ◽  
Author(s):  
Friderike Blumenthal-Barby ◽  
Alf Hamann ◽  
Katja Klugewitz
Keyword(s):  
Single Cell ◽  
Cell Populations ◽  
Single Cell Level ◽  
Cell Level ◽  
Down Regulation ◽  
Th1 Cell

scholarly journals Quantifying the effect of experimental perturbations at single-cell resolution

2019 ◽  
Author(s):  
Daniel B. Burkhardt ◽  
Jay S. Stanley ◽  
Alexander Tong ◽  
Ana Luisa Perdigoto ◽  
Scott A. Gigante ◽  
...  
Keyword(s):  
Single Cell ◽  
Ground Truth ◽  
Likelihood Estimate ◽  
Cell Populations ◽  
Continuous Measure ◽  
Single Cell Level ◽  
Cell Level ◽  
Experimental Conditions ◽  
Transcriptional State ◽  
Multiple Conditions

Abstract Current methods for comparing scRNA-seq datasets collected in multiple conditions focus on discrete regions of the transcriptional state space, such as clusters of cells. Here, we quantify the effects of perturbations at the single-cell level using a continuous measure of the effect of a perturbation across the transcriptomic space. We describe this space as a manifold and develop a relative likelihood estimate of observing each cell in each of the experimental conditions using graph signal processing. This likelihood estimate can be used to identify cell populations specifically affected by a perturbation. We also develop vertex frequency clustering to extract populations of affected cells at the level of granularity that matches the perturbation response. The accuracy of our algorithm to identify clusters of cells that are enriched or depleted in each condition is on average 57% higher than the next best-performing algorithm tested. Gene signatures derived from these clusters are more accurate compared to six alternative algorithms in ground-truth comparisons.

scholarly journals Studying Proteolysis of Cyclin B at the Single Cell Level in Whole Cell Populations

10.3791/4239 ◽  
2012 ◽  
Author(s):  
Dominik Schnerch ◽  
Marie Follo ◽  
Julia Felthaus ◽  
Monika Engelhardt ◽  
Ralph Wäsch
Keyword(s):  
Single Cell ◽  
Cyclin B ◽  
Cell Populations ◽  
Single Cell Level ◽  
Cell Level ◽  
Whole Cell

scholarly journals Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity

2018 ◽  
Author(s):  
Longqi Liu ◽  
Chuanyu Liu ◽  
Andrés Quintero ◽  
Liang Wu ◽  
Yue Yuan ◽  
...  
Keyword(s):  
Single Cell ◽  
Target Genes ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Cell Populations ◽  
Single Cell Level ◽  
Complex Cell ◽  
Cell Level ◽  
Distinct Cell ◽  
Cell Variation

AbstractIntegrative analysis of multi-omics layers at single cell level is critical for accurate dissection of cell-to-cell variation within certain cell populations. Here we report scCAT-seq, a technique for simultaneously assaying chromatin accessibility and the transcriptome within the same single cell. We show that the combined single cell signatures enable accurate construction of regulatory relationships between cis-regulatory elements and the target genes at single-cell resolution, providing a new dimension of features that helps direct discovery of regulatory patterns specific to distinct cell identities. Moreover, we generated the first single cell integrated maps of chromatin accessibility and transcriptome in human pre-implantation embryos and demonstrated the robustness of scCAT-seq in the precise dissection of master transcription factors in cells of distinct states during embryo development. The ability to obtain these two layers of omics data will help provide more accurate definitions of “single cell state” and enable the deconvolution of regulatory heterogeneity from complex cell populations.

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