scholarly journals Capturing single-cell heterogeneity via data fusion improves image-based profiling

2018 ◽  
Author(s):  
Mohammad H. Rohban ◽  
Shantanu Singh ◽  
Anne E. Carpenter
Keyword(s):  
Data Fusion ◽  
Single Cell ◽  
Computational Methods ◽  
Mechanism Of Action ◽  
State Of The Art ◽  
Cell Populations ◽  
Cell Heterogeneity ◽  
Prior State

AbstractSingle-cell resolution technologies warrant computational methods that capture cell heterogeneity while allowing efficient comparisons of populations. Here, we summarize cell populations by adding features’ dispersion and covariances to population averages, in the context of image-based profiling. We find that data fusion is critical for these metrics to improve results over the prior state-of-the-art, providing at least ~20% better performance in predicting a compound’s mechanism of action (MoA) and a gene’s pathway.

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.

scholarly journals Cellular heterogeneity during mouse pancreatic ductal adenocarcinoma progression at single-cell resolution

2019 ◽  
Author(s):  
Abdel Nasser Hosein ◽  
Huocong Huang ◽  
Zhaoning Wang ◽  
Kamalpreet Parmar ◽  
Wenting Du ◽  
...  
Keyword(s):  
Single Cell ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Cancer Cells ◽  
Genetically Engineered ◽  
Cellular Heterogeneity ◽  
Ductal Adenocarcinoma ◽  
Cell Populations ◽  
Specific Cell ◽  
Cell Heterogeneity ◽  
Mesenchymal Transition

AbstractBackground & AimsPancreatic ductal adenocarcinoma (PDA) is a major cause of cancer-related death with limited therapeutic options available. This highlights the need for improved understanding of the biology of PDA progression. The progression of PDA is a highly complex and dynamic process featuring changes in cancer cells and stromal cells; however, a comprehensive characterization of PDA cancer cell and stromal cell heterogeneity during disease progression is lacking. In this study, we aimed to profile cell populations and understand their phenotypic changes during PDA progression.MethodsWe employed single-cell RNA sequencing technology to agnostically profile cell heterogeneity during different stages of PDA progression in genetically engineered mouse models.ResultsOur data indicate that an epithelial-to-mesenchymal transition of cancer cells accompanies tumor progression. We also found distinct populations of macrophages with increasing inflammatory features during PDA progression. In addition, we noted the existence of three distinct molecular subtypes of fibroblasts in the normal mouse pancreas, which ultimately gave rise to two distinct populations of fibroblasts in advanced PDA, supporting recent reports on intratumoral fibroblast heterogeneity. Our data also suggest that cancer cells and fibroblasts are dynamically regulated by epigenetic mechanisms.ConclusionThis study systematically outlines the landscape of cellular heterogeneity during the progression of PDA. It strongly improves our understanding of the PDA biology and has the potential to aid in the development of therapeutic strategies against specific cell populations of the disease.

scholarly journals Quantitative single cell heterogeneity profiling of patient derived tumor initiating gliomaspheres reveals unique signatures of drug response and malignancy

2020 ◽  
Author(s):  
Michael Masterman-Smith ◽  
Nicholas A. Graham ◽  
Ed Panosyan ◽  
Jack Mottahedeh ◽  
Eric E. Samuels ◽  
...  
Keyword(s):  
Single Cell ◽  
Signal Propagation ◽  
Cell Populations ◽  
High Signal ◽  
Cell Heterogeneity ◽  
Cell Array ◽  
Minimal Cell ◽  
Egfr Signaling Pathway ◽  
Egfr Expression ◽  
Growing Cell

AbstractBackgroundGlioblastoma is a deadly brain tumor with median patient survival of 14.6 months. At the core of this malignancy are rare, highly heterogenous malignant stem-like tumor initiating cells. Aberrant signaling across the EGFR-PTEN-AKT-mTOR signal transduction pathways are common oncogenic drivers in these cells. Though gene-level clustering has determined the importance of the EGFR signaling pathway as a treatment indicator, multiparameter protein-level analyses are necessary to discern functional attributes of signal propagation. Multiparameter single cell analyses is emerging as particularly useful in identifying such attributes.MethodsSingle cell targeted proteomic analysis of EGFR-PTEN-AKT-mTOR proteins profiled heterogeneity in a panel of fifteen patient derived gliomaspheres. A microfluidic cell array ‘chip’ tool served as a low cost methodology to derive high quality quantitative single cell analytical outputs. Chip design specifications produced extremely high signal-to-noise ratios and brought experimental efficiencies of cell control and minimal cell use to accommodate experimentation with these rare and often slow-growing cell populations. Quantitative imaging software generated datasets to observe similarities and differences within and between cells and patients. Bioinformatic self-organizing maps (SOMs) and hierarchical clustering stratified patients into malignancy and responder groups which were validated by phenotypic and statistical analyses.ResultsFifteen patient dissociated gliomaspheres produced 59,464 data points from 14,866 cells. Forty-nine molecularly defined signaling phenotypes were identified across samples. Bioinformatics resolved two clusters diverging on EGFR expression (p = 0.0003) and AKT/TORC1 activation (p = 0.08 and p = 0.09 respectively). TCGA status of a subset showed genetic heterogeneity with proneural, classical and mesenchymal subtypes represented in both clusters. Phenotypic validation measures indicated drug responsive phenotypes to EGFR blocking were found in the EGFR expressing cluster. EGFR expression in the subset of drug-treated lines was statistically significant (p<.05). The EGFR expressing cluster was of lower tumor initiating potential in comparison to the AKT/TORC1 activated cluster. Though not statistically significant, EGFR expression trended with improved patient prognosis while AKT/TORC1 activated samples trended with poorer outcomes.ConclusionsQuantitative single cell heterogeneity profiling resolves signaling diversity into meaningful non-obvious phenotypic groups suggesting EGFR is decoupled from AKT/TORC1 signalling while identifying potentially valuable targets for personalized therapeutic approaches for deadly tumor-initiating cell populations.

scholarly journals Single cell transcriptomics of human epidermis reveals basal stem cell transition states

2019 ◽  
Author(s):  
Shuxiong Wang ◽  
Michael L. Drummond ◽  
Christian F. Guerrero-Juarez ◽  
Eric Tarapore ◽  
Adam L. MacLean ◽  
...  
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Single Cell ◽  
Cell Communication ◽  
Cell Populations ◽  
Cell Heterogeneity ◽  
Stable States ◽  
Interfollicular Epidermis ◽  
Epidermal Homeostasis ◽  
Stem Cell Populations

ABSTRACTHow stem cells give rise to human interfollicular epidermis is unclear despite the crucial role the epidermis plays in barrier and appendage formation. Here we use single cell-RNA sequencing to interrogate basal stem cell heterogeneity of human interfollicular epidermis and find at least four spatially distinct stem cell populations that decorate the top and bottom of rete ridge architecture and hold transitional positions between the basal and suprabasal epidermal layers. Cell-cell communication modeling through co-variance of cognate ligand-receptor pairs indicate that the basal cell populations distinctly serve as critical signaling hubs that maintain epidermal communication. Combining pseudotime, RNA velocity, and cellular entropy analyses point to a hierarchical differentiation lineage supporting multi-stem cell interfollicular epidermal homeostasis models and suggest the “transitional” basal stem cells are stable states essential for proper stratification. Finally, alterations in differentially expressed “transitional” basal stem cell genes result in severe thinning of human skin equivalents, validating their essential role in epidermal homeostasis and reinforcing the critical nature of basal stem cell heterogeneity.

scholarly journals Single-cell transcriptome reveals the redifferentiation trajectories of the early stage of de novo shoot regeneration in Arabidopsis thaliana

2022 ◽  
Author(s):  
Guangyu Liu ◽  
Jie Li ◽  
Jiming Li ◽  
Zhiyong Chen ◽  
Peisi Yuan ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Shoot Regeneration ◽  
De Novo ◽  
Developmental Process ◽  
Early Stage ◽  
Cell Populations ◽  
Cell Heterogeneity ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

De novo shoot regeneration from a callus plays a crucial role in both plant biotechnology and the fundamental research of plant cell totipotency. Recent studies have revealed many regulatory factors involved in this developmental process. However. our knowledge of the cell heterogeneity and cell fate transition during de novo shoot regeneration is still limited. Here, we performed time-series single-cell transcriptome experiments to reveal the cell heterogeneity and redifferentiation trajectories during the early stage of de novo shoot regeneration. Based on the single-cell transcriptome data of 35,669 cells at five-time points, we successfully determined seven major cell populations in this developmental process and reconstructed the redifferentiation trajectories. We found that all cell populations resembled root identities and undergone gradual cell-fate transitions. In detail, the totipotent callus cells differentiated into pluripotent QC-like cells and then gradually developed into less differentiated cells that have multiple root-like cell identities, such as pericycle-like cells. According to the reconstructed redifferentiation trajectories, we discovered that the canonical regeneration-related genes were dynamically expressed at certain stages of the redifferentiation process. Moreover, we also explored potential transcription factors and regulatory networks that might be involved in this process. The transcription factors detected at the initial stage, QC-like cells, and the end stage provided a valuable resource for future functional verifications. Overall, this dataset offers a unique glimpse into the early stages of de novo shoot regeneration, providing a foundation for a comprehensive analysis of the mechanism of de novo shoot regeneration.

scholarly journals Capturing single-cell heterogeneity via data fusion improves image-based profiling

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Mohammad H. Rohban ◽  
Hamdah S. Abbasi ◽  
Shantanu Singh ◽  
Anne E. Carpenter
Keyword(s):  
Data Fusion ◽  
Single Cell ◽  
Cell Heterogeneity

Multiparameter single-cell profiling of human CD4+FOXP3+ regulatory T-cell populations in homeostatic conditions and during graft-versus-host disease

Blood ◽  
2013 ◽  
Vol 122 (10) ◽  
pp. 1802-1812 ◽  
Author(s):  
Shen Dong ◽  
Sylvie Maiella ◽  
Aliénor Xhaard ◽  
Yuanyu Pang ◽  
Lynn Wenandy ◽  
...  
Keyword(s):  
Single Cell ◽  
Graft Versus Host Disease ◽  
Cell Populations ◽  
Cell Heterogeneity ◽  
Host Disease ◽  
Graft Versus Host ◽  
Key Points ◽  
Lineage Reprogramming ◽  
Single Cell Profiling ◽  
Acute Graft

Key Points Single-cell heterogeneity, rather than lineage reprogramming, explains the remarkable complexity and functional diversity of human Tregs. Altered homeostasis of Treg subpopulations in patients developing acute graft-versus-host disease.

scholarly journals Cellular heterogeneity during mouse pancreatic ductal adenocarcinoma progression at single-cell resolution.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15739-e15739
Author(s):  
Abdel Nasser Hosein ◽  
Huocong Huang ◽  
Zhaoning Wang ◽  
Kamalpreet Parmar ◽  
Wenting Du ◽  
...  
Keyword(s):  
Single Cell ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Cancer Cells ◽  
Genetically Engineered ◽  
Cellular Heterogeneity ◽  
Ductal Adenocarcinoma ◽  
Cell Populations ◽  
Specific Cell ◽  
Cell Heterogeneity ◽  
Mesenchymal Transition

e15739 Background: Pancreatic ductal adenocarcinoma (PDA) is a major cause of cancer-related death with limited therapeutic options available. This highlights the need for improved understanding of the biology of PDA progression. The progression of PDA is a highly complex and dynamic process featuring changes in cancer cells and stromal cells; however, a comprehensive characterization of PDA cancer cell and stromal cell heterogeneity during disease progression is lacking. In this study, we aimed to profile cell populations and understand their phenotypic changes during PDA progression. Methods: We employed single-cell RNA sequencing technology to agnostically profile cell heterogeneity during different stages of PDA progression in genetically engineered mouse models. Results: Our data indicate that an epithelial-to-mesenchymal transition of cancer cells accompanies tumor progression. We also found distinct populations of macrophages with increasing inflammatory features during PDA progression. In addition, we noted the existence of three distinct molecular subtypes of fibroblasts in the normal mouse pancreas, which ultimately gave rise to two distinct populations of fibroblasts in advanced PDA, supporting recent reports on intratumoral fibroblast heterogeneity. Our data also suggest that cancer cells and fibroblasts are dynamically regulated by epigenetic mechanisms. Conclusions: This study systematically outlines the landscape of cellular heterogeneity during the progression of PDA. It strongly improves our understanding of the PDA biology and has the potential to aid in the development of therapeutic strategies against specific cell populations of the disease.

scholarly journals Discovering Myeloid Cell Heterogeneity in Mandibular Bone – Cell by Cell Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Kyu Hwan Kwack ◽  
Natalie A. Lamb ◽  
Jonathan E. Bard ◽  
Elliot D. Kramer ◽  
Lixia Zhang ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Flow Cytometry ◽  
Single Cell ◽  
Rna Sequencing ◽  
Myeloid Cell ◽  
Cell Populations ◽  
Cell Heterogeneity ◽  
Mandibular Bone ◽  
Single Cell Rna Sequencing ◽  
Myeloid Progenitors

The myeloid-derived bone marrow progenitor populations from different anatomical locations are known to have diverse osteoclastogenesis potential. Specifically, myeloid progenitors from the tibia and femur have increased osteoclast differentiation potential compared to myeloid progenitors from the alveolar process. In this study, we explored the differences in the myeloid lineage progenitor cell populations in alveolar (mandibular) bone versus long (femur) bone using flow cytometry and high-throughput single cell RNA sequencing (scRNA-seq) to provide a comprehensive transcriptional landscape. Results indicate that mandibular bone marrow-derived cells exhibit consistent deficits in myeloid differentiation, including significantly fewer myeloid-derived suppressor cell (MDSC)-like populations (CD11b+Ly6C+, CD11b+Ly6G+), as well as macrophages (CD11b+F4/80+). Although significantly fewer in number, MDSCs from mandibular bone exhibited increased immunosuppressive activity compared to MDSCs isolated from long bone. Using flow cytometry panels specific for bone marrow progenitors, analysis of hematopoietic stem cells showed no defects in mandibular bone marrow in LSK (Lin–Sca1+cKit+) cell and LK (Lin–Sca1–cKit+) cell populations. While there was no significant difference in granulocyte progenitors, the granulocyte-monocyte progenitors and monocyte progenitor population were significantly decreased in the mandibular bone marrow. T-lymphocyte subsets were not significantly different between mandibular and femoral bone, except for CD4+CD25+Foxp3+ regulatory T lymphocytes, which were significantly increased in the mandible. In addition, B lymphocytes were significantly increased in mandible. Single cell RNA sequencing from mandible and femur BM revealed distinct differences in transcriptomic profiles in myeloid populations establishing previously unappreciated aspects of mandibular bone marrow populations. These analyses reveal site-specific differences in the myeloid progenitor cellular composition and transcriptional programs providing a deeper appreciation of the complex differences in myeloid cell heterogeneity from different anatomical bone marrow sites.

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