Challenges and Opportunities for the Translation of Single-Cell RNA Sequencing Technologies to Dermatology

Skin is a complex and heterogeneous organ at the cellular level. This complexity is beginning to be understood through the application of single-cell genomics and computational tools. A large number of datasets that shed light on how the different human skin cell types interact in homeostasis—and what ceases to work in diverse dermatological diseases—have been generated and are publicly available. However, translation of these novel aspects to the clinic is lacking. This review aims to summarize the state-of-the-art of skin biology using single-cell technologies, with a special focus on skin pathologies and the translation of mechanistic findings to the clinic. The main implications of this review are to summarize the benefits and limitations of single-cell analysis and thus help translate the emerging insights from these novel techniques to the bedside.

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Single-Cell Sequencing Reveals Differential Cell Types in Skin Tissues of Liaoning Cashmere Goats and Key Genes Related Potentially to the Fineness of Cashmere Fiber

Frontiers in Genetics ◽

10.3389/fgene.2021.726670 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zeying Wang ◽

Yanru Wang ◽

Taiyu Hui ◽

Rui Chen ◽

Yanan Xu ◽

...

Keyword(s):

Single Cell ◽

Computational Models ◽

Marker Gene ◽

Dermal Papilla ◽

Cell Types ◽

Cellular Level ◽

Dermal Papilla Cells ◽

Skin Cell ◽

Cashmere Goats ◽

Cell Trajectory

Cashmere fineness is one of the important factors determining cashmere quality; however, our understanding of the regulation of cashmere fineness at the cellular level is limited. Here, we used single-cell RNA sequencing and computational models to identify 13 skin cell types in Liaoning cashmere goats. We also analyzed the molecular changes in the development process by cell trajectory analysis and revealed the maturation process in the gene expression profile in Liaoning cashmere goats. Weighted gene co-expression network analysis explored hub genes in cell clusters related to cashmere formation. Secondary hair follicle dermal papilla cells (SDPCs) play an important role in the growth and density of cashmere. ACTA2, a marker gene of SDPCs, was selected for immunofluorescence (IF) and Western blot (WB) verification. Our results indicate that ACTA2 is mainly expressed in SDPCs, and WB results show different expression levels. COL1A1 is a highly expressed gene in SDPCs, which was verified by IF and WB. We then selected CXCL8 of SDPCs to verify and prove the differential expression in the coarse and fine types of Liaoning cashmere goats. Therefore, the CXCL8 gene may regulate cashmere fineness. These genes may be involved in regulating the fineness of cashmere in goat SDPCs; our research provides new insights into the mechanism of cashmere growth and fineness regulation by cells.

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Automated quality control and cell identification of droplet-based single-cell data using dropkick

10.1101/2020.10.08.332288 ◽

2020 ◽

Author(s):

Cody N. Heiser ◽

Victoria M. Wang ◽

Bob Chen ◽

Jacob J. Hughey ◽

Ken S. Lau

Keyword(s):

Quality Control ◽

Single Cell ◽

Single Cell Analysis ◽

Software Tool ◽

Cell Types ◽

Computational Method ◽

High Background ◽

Cell Identification ◽

Sequencing Technologies ◽

Downstream Analysis

AbstractA major challenge for droplet-based single-cell sequencing technologies is distinguishing true cells from uninformative barcodes in datasets with disparate library sizes confounded by high technical noise (i.e. batch-specific ambient RNA). We present dropkick, a fully automated software tool for quality control and filtering of single-cell RNA sequencing (scRNA-seq) data with a focus on excluding ambient barcodes and recovering real cells bordering the quality threshold. By automatically determining dataset-specific training labels based on predictive global heuristics, dropkick learns a gene-based representation of real cells and ambient noise, calculating a cell probability score for each barcode. Using simulated and real-world scRNA-seq data, we benchmarked dropkick against a conventional thresholding approach and EmptyDrops, a popular computational method, demonstrating greater recovery of rare cell types and exclusion of empty droplets and noisy, uninformative barcodes. We show for both low and high-background datasets that dropkick’s weakly supervised model reliably learns which genes are enriched in ambient barcodes and draws a multidimensional boundary that is more robust to dataset-specific variation than existing filtering approaches. dropkick provides a fast, automated tool for reproducible cell identification from scRNA-seq data that is critical to downstream analysis and compatible with popular single-cell analysis Python packages.

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Single-cell analysis of human adipose tissue identifies depot- and disease-specific cell types

Nature Metabolism ◽

10.1038/s42255-019-0152-6 ◽

2019 ◽

Vol 2 (1) ◽

pp. 97-109 ◽

Cited By ~ 17

Author(s):

Jinchu Vijay ◽

Marie-Frédérique Gauthier ◽

Rebecca L. Biswell ◽

Daniel A. Louiselle ◽

Jeffrey J. Johnston ◽

...

Keyword(s):

Adipose Tissue ◽

Single Cell ◽

Single Cell Analysis ◽

Human Adipose Tissue ◽

Cell Types ◽

Specific Cell ◽

Cell Analysis ◽

Disease Specific

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Advances in Single-Cell Printing

Micromachines ◽

10.3390/mi13010080 ◽

2022 ◽

Vol 13 (1) ◽

pp. 80

Author(s):

Xiaohu Zhou ◽

Han Wu ◽

Haotian Wen ◽

Bo Zheng

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Three Dimensional ◽

Cell Isolation ◽

Special Focus ◽

Cell Analysis ◽

Cell Array ◽

Cell Printing ◽

Recent Developments ◽

Single Cell Isolation

Single-cell analysis is becoming an indispensable tool in modern biological and medical research. Single-cell isolation is the key step for single-cell analysis. Single-cell printing shows several distinct advantages among the single-cell isolation techniques, such as precise deposition, high encapsulation efficiency, and easy recovery. Therefore, recent developments in single-cell printing have attracted extensive attention. We review herein the recently developed bioprinting strategies with single-cell resolution, with a special focus on inkjet-like single-cell printing. First, we discuss the common cell printing strategies and introduce several typical and advanced printing strategies. Then, we introduce several typical applications based on single-cell printing, from single-cell array screening and mass spectrometry-based single-cell analysis to three-dimensional tissue formation. In the last part, we discuss the pros and cons of the single-cell strategies and provide a brief outlook for single-cell printing.

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Single-cell analysis revealed that IL4I1 promoted ovarian cancer progression

Journal of Translational Medicine ◽

10.1186/s12967-021-03123-7 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Hongyu Zhao ◽

Yu Teng ◽

Wende Hao ◽

Jie Li ◽

Zhefeng Li ◽

...

Keyword(s):

Ovarian Cancer ◽

Single Cell ◽

Cancer Progression ◽

Cox Regression ◽

Single Cell Analysis ◽

Cell Types ◽

In Vitro Assays ◽

Migration And Invasion ◽

Dominant Type

Abstract Background Ovarian cancer was one of the leading causes of female deaths. Patients with OC were essentially incurable and portends a poor prognosis, presumably because of profound genetic heterogeneity limiting reproducible prognostic classifications. Methods We comprehensively analyzed an ovarian cancer single-cell RNA sequencing dataset, GSE118828, and identified nine major cell types. Relationship between the clusters was explored with CellPhoneDB. A malignant epithelial cluster was confirmed using pseudotime analysis, CNV and GSVA. Furthermore, we constructed the prediction model (i.e., RiskScore) consisted of 10 prognosis-specific genes from 2397 malignant epithelial genes using the LASSO Cox regression algorithm based on public datasets. Then, the prognostic value of Riskscore was assessed with Kaplan–Meier survival analysis and time-dependent ROC curves. At last, a series of in-vitro assays were conducted to explore the roles of IL4I1, an important gene in Riskscore, in OC progression. Results We found that macrophages possessed the most interaction pairs with other clusters, and M2-like TAMs were the dominant type of macrophages. C0 was identified as the malignant epithelial cluster. Patients with a lower RiskScore had a greater OS (log-rank P < 0.01). In training set, the AUC of RiskScore was 0.666, 0.743 and 0.809 in 1-year, 3-year and 5-year survival, respectively. This was also validated in another two cohorts. Moreover, downregulation of IL4I1 inhibited OC cells proliferation, migration and invasion. Conclusions Our work provide novel insights into our understanding of the heterogeneity among OCs, and would help elucidate the biology of OC and provide clinical guidance in prognosis for OC patients.

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Single-cell analysis reveals cellular heterogeneity and molecular determinants of hypothalamic leptin-receptor cells

10.1101/2020.07.23.217729 ◽

2020 ◽

Author(s):

N. Kakava-Georgiadou ◽

J.F. Severens ◽

A.M. Jørgensen ◽

K.M. Garner ◽

M.C.M Luijendijk ◽

...

Keyword(s):

Single Cell ◽

Leptin Receptor ◽

Single Cell Analysis ◽

Cell Types ◽

Cellular Heterogeneity ◽

Molecular Signature ◽

Neuronal Populations ◽

Hypothalamic Nuclei ◽

Satiety Hormone ◽

Multiple Cell

AbstractHypothalamic nuclei which regulate homeostatic functions express leptin receptor (LepR), the primary target of the satiety hormone leptin. Single-cell RNA sequencing (scRNA-seq) has facilitated the discovery of a variety of hypothalamic cell types. However, low abundance of LepR transcripts prevented further characterization of LepR cells. Therefore, we perform scRNA-seq on isolated LepR cells and identify eight neuronal clusters, including three uncharacterized Trh-expressing populations as well as 17 non-neuronal populations including tanycytes, oligodendrocytes and endothelial cells. Food restriction had a major impact on Agrp neurons and changed the expression of obesity-associated genes. Multiple cell clusters were enriched for GWAS signals of obesity. We further explored changes in the gene regulatory landscape of LepR cell types. We thus reveal the molecular signature of distinct populations with diverse neurochemical profiles, which will aid efforts to illuminate the multi-functional nature of leptin’s action in the hypothalamus.

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Single cell analysis reveals immune cell–adipocyte crosstalk regulating the transcription of thermogenic adipocytes

eLife ◽

10.7554/elife.49501 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 17

Author(s):

Prashant Rajbhandari ◽

Douglas Arneson ◽

Sydney K Hart ◽

In Sook Ahn ◽

Graciel Diamante ◽

...

Keyword(s):

Single Cell ◽

Immune Cells ◽

Immune Cell ◽

Single Cell Analysis ◽

Metabolic Response ◽

Cell Types ◽

Specific Cell ◽

Beneficial Effects ◽

Thermogenic Adipocytes ◽

And Function

Immune cells are vital constituents of the adipose microenvironment that influence both local and systemic lipid metabolism. Mice lacking IL10 have enhanced thermogenesis, but the roles of specific cell types in the metabolic response to IL10 remain to be defined. We demonstrate here that selective loss of IL10 receptor α in adipocytes recapitulates the beneficial effects of global IL10 deletion, and that local crosstalk between IL10-producing immune cells and adipocytes is a determinant of thermogenesis and systemic energy balance. Single Nuclei Adipocyte RNA-sequencing (SNAP-seq) of subcutaneous adipose tissue defined a metabolically-active mature adipocyte subtype characterized by robust expression of genes involved in thermogenesis whose transcriptome was selectively responsive to IL10Rα deletion. Furthermore, single-cell transcriptomic analysis of adipose stromal populations identified lymphocytes as a key source of IL10 production in response to thermogenic stimuli. These findings implicate adaptive immune cell-adipocyte communication in the maintenance of adipose subtype identity and function.

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Methods and sensors for functional genomic studies of cell-cycle transitions in single cells

Physiological Genomics ◽

10.1152/physiolgenomics.00065.2020 ◽

2020 ◽

Vol 52 (10) ◽

pp. 468-477

Author(s):

Alexander C. Zambon ◽

Tom Hsu ◽

Seunghee Erin Kim ◽

Miranda Klinck ◽

Jennifer Stowe ◽

...

Keyword(s):

Cell Cycle ◽

Single Cell ◽

Single Cell Analysis ◽

Imaging System ◽

Single Cells ◽

Cell Types ◽

Cell Analysis ◽

Mcf7 Cells ◽

Genomic Studies ◽

Cell Subpopulations

Much of our understanding of the regulatory mechanisms governing the cell cycle in mammals has relied heavily on methods that measure the aggregate state of a population of cells. While instrumental in shaping our current understanding of cell proliferation, these approaches mask the genetic signatures of rare subpopulations such as quiescent (G0) and very slowly dividing (SD) cells. Results described in this study and those of others using single-cell analysis reveal that even in clonally derived immortalized cancer cells, ∼1–5% of cells can exhibit G0 and SD phenotypes. Therefore to enable the study of these rare cell phenotypes we established an integrated molecular, computational, and imaging approach to track, isolate, and genetically perturb single cells as they proliferate. A genetically encoded cell-cycle reporter (K67p-FUCCI) was used to track single cells as they traversed the cell cycle. A set of R-scripts were written to quantify K67p-FUCCI over time. To enable the further study G0 and SD phenotypes, we retrofitted a live cell imaging system with a micromanipulator to enable single-cell targeting for functional validation studies. Single-cell analysis revealed HT1080 and MCF7 cells had a doubling time of ∼24 and ∼48 h, respectively, with high duration variability in G1 and G2 phases. Direct single-cell microinjection of mRNA encoding (GFP) achieves detectable GFP fluorescence within ∼5 h in both cell types. These findings coupled with the possibility of targeting several hundreds of single cells improves throughput and sensitivity over conventional methods to study rare cell subpopulations.

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Stratification of chemotherapy-treated stage III colorectal cancer patients using multiplexed imaging and single-cell analysis of T-cell populations

Modern Pathology ◽

10.1038/s41379-021-00953-0 ◽

2021 ◽

Author(s):

Xanthi Stachtea ◽

Maurice B. Loughrey ◽

Manuela Salvucci ◽

Andreas U. Lindner ◽

Sanghee Cho ◽

...

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

Single Cell ◽

Immune Cell ◽

Single Cell Analysis ◽

Cell Types ◽

Cell Segmentation ◽

Stage Iii ◽

Cell Analysis ◽

Increased Risk

AbstractColorectal cancer (CRC) has one of the highest cancer incidences and mortality rates. In stage III, postoperative chemotherapy benefits <20% of patients, while more than 50% will develop distant metastases. Biomarkers for identification of patients at increased risk of disease recurrence following adjuvant chemotherapy are currently lacking. In this study, we assessed immune signatures in the tumor and tumor microenvironment (TME) using an in situ multiplexed immunofluorescence imaging and single-cell analysis technology (Cell DIVETM) and evaluated their correlations with patient outcomes. Tissue microarrays (TMAs) with up to three 1 mm diameter cores per patient were prepared from 117 stage III CRC patients treated with adjuvant fluoropyrimidine/oxaliplatin (FOLFOX) chemotherapy. Single sections underwent multiplexed immunofluorescence staining for immune cell markers (CD45, CD3, CD4, CD8, FOXP3, PD1) and tumor/cell segmentation markers (DAPI, pan-cytokeratin, AE1, NaKATPase, and S6). We used annotations and a probabilistic classification algorithm to build statistical models of immune cell types. Images were also qualitatively assessed independently by a Pathologist as ‘high’, ‘moderate’ or ‘low’, for stromal and total immune cell content. Excellent agreement was found between manual assessment and total automated scores (p < 0.0001). Moreover, compared to single markers, a multi-marker classification of regulatory T cells (Tregs: CD3+/CD4+FOXP3+/PD1−) was significantly associated with disease-free survival (DFS) and overall survival (OS) (p = 0.049 and 0.032) of FOLFOX-treated patients. Our results also showed that PD1− Tregs rather than PD1+ Tregs were associated with improved survival. These findings were supported by results from an independent FOLFOX-treated cohort of 191 stage III CRC patients, where higher PD1− Tregs were associated with an increase overall survival (p = 0.015) for CD3+/CD4+/FOXP3+/PD1−. Overall, compared to single markers, multi-marker classification provided more accurate quantitation of immune cell types with stronger correlations with outcomes.

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Automated Classification of Breast Cancer Cells Using High-Throughput Holographic Cytometry

Frontiers in Physics ◽

10.3389/fphy.2021.759142 ◽

2021 ◽

Vol 9 ◽

Author(s):

Cindy X. Chen ◽

Han Sang Park ◽

Hillel Price ◽

Adam Wax

Keyword(s):

Single Cell ◽

Cancer Cells ◽

High Throughput ◽

Single Cell Analysis ◽

Imaging Modality ◽

Cell Types ◽

Needle Aspiration ◽

Microfluidic Channels ◽

Phase Imaging ◽

Imaging Data

Holographic cytometry is an ultra-high throughput quantitative phase imaging modality that is capable of extracting subcellular information from millions of cells flowing through parallel microfluidic channels. In this study, we present our findings on the application of holographic cytometry to distinguishing carcinogen-exposed cells from normal cells and cancer cells. This has potential application for environmental monitoring and cancer detection by analysis of cytology samples acquired via brushing or fine needle aspiration. By leveraging the vast amount of cell imaging data, we are able to build single-cell-analysis-based biophysical phenotype profiles on the examined cell lines. Multiple physical characteristics of these cells show observable distinct traits between the three cell types. Logistic regression analysis provides insight on which traits are more useful for classification. Additionally, we demonstrate that deep learning is a powerful tool that can potentially identify phenotypic differences from reconstructed single-cell images. The high classification accuracy levels show the platform’s potential in being developed into a diagnostic tool for abnormal cell screening.

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