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Single-Cell Analysis

Single-Cell Analysis (SCA) is a powerful technique that enables the study of individual cells within a heterogeneous population.
This approach provides a deep and nuanced understanding of cellular diversity, function, and signaling pathways.
SCA leverages advanced technologies, such as flow cytometry, microscopy, and next-generation sequencing, to analyze the unique properties of individual cells, including their gene expression, epigenetic profiles, and metabolic activities.
By focusing on the individual cell, researchers can uncover rare cell types, detect subtle cellular changes, and gain insights into complex biological processes that were previously inaccessiblr.
SCA has become an invaluable tool in fields like immunology, oncology, neuroscience, and developmental biology, allowing for the identification of novel cell types, the elucidation of disease mechanisms, and the development of targeted therapies.
With its growing importance in biomedical research, SCA continues to push the boundaries of our understanding of the fundamental unit of life – the single cell.

Most cited protocols related to «Single-Cell Analysis»

1
Standardized Single-Cell RNA-seq Normalization
Cited 1013 times
We downloaded raw read or UMI matrices for all datasets, from their respective sources. The one exception was the 3pV1 dataset from the PBMC analysis. These data were originally quantified with the hg19 reference, while the other two PBMC datasets were quantified with GRCh38. Thus, we downloaded the fastq files from the 10X website (Supplementary Table 8). We quantified gene expression counts using Cell Ranger11 ,41 v2.1.0 with GRCh38. From the raw count matrices, we used a standard data normalization procedure, laid out below, for all analyses, unless otherwise specified. Except for the L2 normalization and within-batch variable gene detection, this procedure follows the standard guidelines of the Seurat single cell analysis platform.
We filtered cells with fewer than 500 genes or more than 20% mitochondrial reads. In the pancreas datasets, we filtered cells with the same thresholds used in Butler et al7 : 1750 genes for CelSeq, 2500 genes for CelSeq2, no filter for Fluidigm C1, 2500 genes for SmartSeq2, and 500 genes for inDrop. We then library normalized each cell to 10,000 reads, by multiplicative scaling, then log scaled the normalized data. We then identified the top 1000 variable genes, ranked by coefficient of variation, within in each dataset. We pooled these genes to form the variable gene set of the analysis. Using only the variable genes, we mean centered and variance 1 scaled the genes across the cells. Note that this was done in the aggregate matrix, with all cells, rather than within each dataset separately. With these values, we performed truncated SVD keeping the top 30 eigenvectors. Finally, we multiplied the cell embeddings by the eigenvalues to avoid giving eigenvectors equal variance.
Korsunsky I., Millard N., Fan J., Slowikowski K., Zhang F., Wei K., Baglaenko Y., Brenner M., Loh P.R, & Raychaudhuri S. (2019). Fast, sensitive, and accurate integration of single cell data with Harmony. Nature methods, 16(12), 1289-1296.
Publication 2019
DNA Library Gene Expression Genes Mitochondrial Inheritance Pancreas Single-Cell Analysis
2
Standardized Single-Cell RNA-seq Normalization
Cited 721 times
We downloaded raw read or UMI matrices for all datasets, from their respective sources. The one exception was the 3pV1 dataset from the PBMC analysis. These data were originally quantified with the hg19 reference, while the other two PBMC datasets were quantified with GRCh38. Thus, we downloaded the fastq files from the 10X website (Supplementary Table 8). We quantified gene expression counts using Cell Ranger11 ,41 v2.1.0 with GRCh38. From the raw count matrices, we used a standard data normalization procedure, laid out below, for all analyses, unless otherwise specified. Except for the L2 normalization and within-batch variable gene detection, this procedure follows the standard guidelines of the Seurat single cell analysis platform.
We filtered cells with fewer than 500 genes or more than 20% mitochondrial reads. In the pancreas datasets, we filtered cells with the same thresholds used in Butler et al7 : 1750 genes for CelSeq, 2500 genes for CelSeq2, no filter for Fluidigm C1, 2500 genes for SmartSeq2, and 500 genes for inDrop. We then library normalized each cell to 10,000 reads, by multiplicative scaling, then log scaled the normalized data. We then identified the top 1000 variable genes, ranked by coefficient of variation, within in each dataset. We pooled these genes to form the variable gene set of the analysis. Using only the variable genes, we mean centered and variance 1 scaled the genes across the cells. Note that this was done in the aggregate matrix, with all cells, rather than within each dataset separately. With these values, we performed truncated SVD keeping the top 30 eigenvectors. Finally, we multiplied the cell embeddings by the eigenvalues to avoid giving eigenvectors equal variance.
Korsunsky I., Millard N., Fan J., Slowikowski K., Zhang F., Wei K., Baglaenko Y., Brenner M., Loh P.R, & Raychaudhuri S. (2019). Fast, sensitive, and accurate integration of single cell data with Harmony. Nature methods, 16(12), 1289-1296.
Publication 2019
DNA Library Gene Expression Genes Mitochondrial Inheritance Pancreas Single-Cell Analysis
3
Single-Cell RNA-seq Normalization Protocol
Cited 547 times
For all single cell analysis, we performed the same initial normalization. Gene expression values for each cell were scaled by the total number of transcripts and multiplied by 10,000. These scaled expression data were then natural-log transformed using log1p before further downstream analyses. After normalization, we calculated scaled expression (z-scores for each gene) for downstream dimensional reduction.
Butler A., Hoffman P., Smibert P., Papalexi E, & Satija R. (2018). Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature biotechnology, 36(5), 411-420.
Publication 2018
Cells Gene Expression Genes Single-Cell Analysis
4
Single-Cell RNA Sequencing of Brain Tissue
Cited 215 times
All methods and relevant materials are discussed in detail in SI Appendix, SI Methods. The single cell analysis pipeline consisted of the following steps. Brain tissue was dissociated and single cell suspensions were loaded on a medium-sized C1 Single-Cell Auto Prep Array for mRNA Seq available (Fluidigm). cDNA was converted into sequencing libraries using a Nextera XT DNA Sample Preparation Kit (Illumina). Raw sequencing reads were aligned using STAR and per gene counts were calculated using HTSEQ. Gene counts were further analyzed using R. Patients were consented for the acquisition of specimens through a process approved by the Stanford Hospital Institutional Review Board.
Darmanis S., Sloan S.A., Zhang Y., Enge M., Caneda C., Shuer L.M., Hayden Gephart M.G., Barres B.A, & Quake S.R. (2015). A survey of human brain transcriptome diversity at the single cell level. Proceedings of the National Academy of Sciences of the United States of America, 112(23), 7285-7290.
Publication 2015
Brain Cells DNA, Complementary Ethics Committees, Research Genes Patients RNA, Messenger Single-Cell Analysis Tissues
5
Retinal Cell Clustering Analysis
Cited 125 times

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Publication 2015
Cells Esters Gene Expression Gene Library Genes Retina Single-Cell Analysis Trees

Most recents protocols related to «Single-Cell Analysis»

1
Spatial-Temporal Analysis of Tumor Immune Microenvironment
The Tumor Immune Single Cell Hub (TISCH) was utilized to conduct single-cell analyses [18 (link)] to determine which BC cell types may express IMMT. Next, independent datasets from the scTIME Portal [19 (link)], consisting of BC patients’ cells, were analyzed on GSE75688 and visualized in UMAP. A heatmap was used to illustrate the signature expression of Mitophagy in GSE75688. The cellular communication among various T cell subsets was analyzed by the LR network of the scTIME Portal. SpatialDB was used to analyze the spatial transcriptomics [20 (link)], whereby the gene expression in tissue sections can be visualized and quantified. The IMMT expression in the immune cells of BC tissue was determined based on the GSE114724 dataset. TISIDB was used to conduct the Spearman correlation test for IMMT expression with immune infiltration levels [21 (link)]. GEPIA2 was used to determine the correlation of IMMT with the immune cell signature [22 (link)]. Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) was used to evaluate the matrix content, immune cell infiltration levels, comprehensive score, and tumor purity [23 (link)].
Lin H.Y., Wu H.J, & Chu P.Y. (2023). Multi-omics and experimental analysis unveil theragnostic value and immunological roles of inner membrane mitochondrial protein (IMMT) in breast cancer. Journal of Translational Medicine, 21, 189.
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Publication 2023
Cells Gene Expression Gene Expression Profiling Malignant Neoplasms Mitophagy Neoplasms Patients Single-Cell Analysis T-Lymphocyte Subsets Tissues
2
Single-cell transcriptome profiling of spermatogenesis
Cited 1 time
Raw reads were processed to generate gene expression profiles using an internal pipeline. Briefly, for each cell barcode the unique molecular identifier (UMI) was extracted after filtering read one without poly-T tails. Adapters and poly-A tails were trimmed (fastp V1) before aligning read two to GRCh38 with Drosophila melanogaster Ensembl version 102 annotation [58 (link)]. For reads with the same cell barcode, the UMI and gene were grouped together to calculate the number of UMIs for genes in each cell. UMI count tables for each cellular barcode were employed for further analysis. Cells with an unusually high number of UMIs (>37,000) or mitochondrial gene percent (>25%) were filtered out. We also excluded cells with less than 990 or more than 4200 genes detected.
Cell type identification and clustering analysis were performed using the Seurat program [59 (link), 60 (link)]. Cell-by-gene matrices for each sample were individually imported to Seurat version 3.1.1 for downstream analysis [60 (link)]. Uniform manifold approximation and projection (UMAP) and t-distributed Stochastic Neighbor Embedding (t-SNE) were performed to visualize cell clusters. Upregulated enriched genes were determined to be significant with a threshold standard of fold change >1.28 and a P-value <0.01. Differentially expressed genes (DEGs) were considered significant with a fold change >1.50 and P-value <0.05.
Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out on the gene set using clusterProfiler software to explore biological functions or pathways significantly associated with specifically expressed genes [61 (link)].
For correlation analysis, gene sets were calculated based on average expression counts that belong to each set of features via the PercentageFeatureSet function in the Seurat package [46 (link)]. Pearson correlations were calculated among these gene sets or signatures. Gene correlation analysis was performed directly on the data matrix by the Pearson correlation method.
Monocle 2 (version 2.10.1) was used to perform single cell trajectory analysis based on the matrix of cells and gene expression [62 (link)]. Monocle 2 reduced the space down to one with two dimensions and ordered the cells [63 (link)]. Once the cells were ordered, the trajectory was visualized in the reduced dimensional space. Pseudotime trajectory analysis was used to further analyze the germ cell differentiation trajectories to identify key factors or pathways required for different novel stages during spermatogenesis.
Yu J., Fu Y., Li Z., Huang Q., Tang J., Sun C., Zhou P., He L., Sun F., Cheng X., Ji L., Yu H., Shi Y., Gu Z., Sun F, & Zhao X. (2023). Single-cell RNA sequencing reveals cell landscape following antimony exposure during spermatogenesis in Drosophila testes. Cell Death Discovery, 9, 86.
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Publication 2023
Biological Processes Cells Cytosol Differentiations, Cell Drosophila melanogaster Gene Expression Genes Genes, Mitochondrial Genome Poly(A) Tail Poly T Single-Cell Analysis Spermatogenesis Tail
3
Single-Cell RNA-seq Analysis Pipeline
We extracted the single-cell RNA sequencing data used in this paper from Gene Expression Omnibus (GEO; GSE138826) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138826; GSE138826_expression_matrix.txt) (Oprescu et al., 2020 (link)). The preliminary analyses of processed scRNA-seq data were analysed using the Seurat suite (version 4.0.3) standard workflow in RStudio Version 1.2.5042 and R version 4.0.3. First, we applied initial quality control to Oprescu et al., 2020 (link) dataset. We kept all the features (genes) expressed at least in five cells and cells with more than 200 genes detected. Otherwise, we filtered out the cells. Second, we verified nUMIs_RNA (>200 and < 4,000) and percent.mt. (less than 5%) Third, UMIs were normalized to counts-per-ten-thousand log-transformed (normalization.method = LogNormalize). The log-normalized data were then used to find variable genes (FindVariableFeatures) and scaled (ScaleData). Finally, Principal Component Analysis (PCA) was run (RunPCA) on the scaled data using highly variable features or genes. Elbowplot were used to decide the number of principal components (PCs) to use for unsupervised graph-based clustering and dimensional reduction plot (UMAP) embedding of all cells or further subclustering analyses (i.e., FAPs) using the standard FindNeighbors, FindClusters, and RunUMAP workflow. We used 30 PCs and a resolution of 0.6 to visualize a Uniform manifold approximation and projection (UMAP) dimensionality reduction plot generated on the same set of PCs used for clustering. We decided the resolution value for FindClusters on a supervised basis after considering clustering output from a range of resolutions (0.4, 0.6, 0.8, and 1.2). We used a resolution of 0.6. Our initial clustering analysis returned 29 clusters (clusters 0–28). We identified cell populations and lineage-specific marker genes for the analyzed dataset using the FindAllMarkers function with logfc.threshold = 0.25, test.use = “wilcox,” and max.cells.per.ident = 1,000. We then plotted the top 10 expressed genes, grouped by orig.ident and seurat_clusters using the DoHeatmap function. We determine cell lineages and cell types based on the expression of canonical genes. We also inspected the clusters (in Figures 2, 3) for hybrid or not well-defined gene expression signatures. Clusters that had similar canonical marker gene expression patterns were merged.
For Mesenchymal Clusters (group of FAPs + DiffFibroblasts + Tenocytes obtained in Figure 2) we used PCs 20 and a resolution of 20 to visualize on the UMAP plot. Our mesenchymal subclustering analysis returned 10 clusters (clusters 0–9). Cell populations and lineage-specific marker genes were identified for the analyzed dataset using the FindAllMarkers function with logfc.threshold = 0.25 and max.cells.per.ident = 1,000. We then plotted the top eight expressed genes, grouped by orig.ident and seurat_clusters using the DoHeatmap function. The identity of the returned cell clusters was then annotated based on known marker genes (see details about cell type and cell lineage definitions in the main text, Results section). Individual cell clusters were grouped to represent cell lineages and types better. Finally, figures were generated using Seurat and ggplot2 R packages. We also used dot plots because they reveal gross differences in expression patterns across different cell types and highlight moderately or highly expressed genes.
To validate our initial skeletal muscle single-cell analysis, we explored three publicly available scRNAseq datasets (McKellar et al., 2021 (link); Yang et al., 2022 (link); Zhang et al., 2022 (link)). Zhang et al. dataset was explored using R/ShinyApp (https://mayoxz.shinyapps.io/Muscle), McKellar et al. (2021) (link) using their web tool developed http://scmuscle.bme.cornell.edu/, and Yang et al. using their Single Cell Metab Browser http://scmetab.mit.edu/. All the figures used were downloaded from the websites (Supplementary Figure S6).
The scRNAseq pipeline used for MuSC subclustering was developed following previous studies (Oprescu et al., 2020 (link); Contreras et al., 2021a (link)). To perform unsupervised MuSC subclustering, we used Seurat’s subset function FindClusters, followed by dimensionality reduction and UMAP visualization (DimPlot) in Seurat.
Contreras O, & Harvey R.P. (2023). Single-cell transcriptome dynamics of the autotaxin-lysophosphatidic acid axis during muscle regeneration reveal proliferative effects in mesenchymal fibro-adipogenic progenitors. Frontiers in Cell and Developmental Biology, 11, 1017660.
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Publication 2023
Cells FAP protein, human Gene Expression Genes Genetic Markers Hybrids Mesenchyma Muscle Tissue Single-Cell Analysis Single-Cell RNA-Seq Skeletal Muscles Tenocytes
4
Single-cell analysis of AML samples
AML blasts were counted and resuspended at the appropriate concentration for loading into the Chromium 10X single-cell 3’ Gene Expression v3 chip according to the manufacturer’s protocol. 3′ gene expression libraries construction and sequencing on Illumina NovaSeq S2 were performed following the manufacturer’s indications. Sequenced libraries were demultiplexed and processed by Cell Ranger Single-Cell Software Suite (version 6.1.1, 10X Genomics) using GRCh38 reference genome and gene annotations (v3.0.0) provided by the manufacturer. We retrieved 44,170 cells over 8 samples with a median of 51,705 mean reads per cell and 9976 Unique Molecular Identifier (UMI) median counts per cell. We first ran a preliminary analysis using Seurat R package (v 3.2.3) on each single-patient dataset independently to discriminate AML cells (characterized by monosomy of chromosome 7 (Chr 7)) from their normal hematopoietic counterpart (non-AML). We leveraged the AddModuleScore function for evaluating the expression level of a Chr 7 signature by using as input gene list all genes located on it. The observed distribution of Chr 7 module scores in the datasets followed a bimodal distribution allowing us to classify cells as AML or non-AML by running a k-means clustering (n = 2) on the vector of Chr 7 signature scores and labelling cells in the high score group as non-AML and those in the low score group as AML (Supplementary Fig. 7D). We then subset the full dataset with AML-only cells. Single-cell data analysis was performed using the same workflow described for NPM1mut AMLs, except for harmony batch removal which accounted for both patient- and timepoint-dependent batch effects (orig.ident variable) and for dimensionality reduction computed on the top 30 Harmony corrected principal components.
Naldini M.M., Casirati G., Barcella M., Rancoita P.M., Cosentino A., Caserta C., Pavesi F., Zonari E., Desantis G., Gilioli D., Carrabba M.G., Vago L., Bernardi M., Di Micco R., Di Serio C., Merelli I., Volpin M., Montini E., Ciceri F, & Gentner B. (2023). Longitudinal single-cell profiling of chemotherapy response in acute myeloid leukemia. Nature Communications, 14, 1285.
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Publication 2023
Chromium Chromosome 7, monosomy Chromosomes, Human, Pair 7 Cloning Vectors DNA Chips Gene Annotation Gene Expression Gene Library Genes Genome Hematopoietic System Leukemia, Myelocytic, Acute Patients Single-Cell Analysis
5
Analyzing Human PFC snRNA-seq Data
The snRNA-seq datasets (raw or gene expression matrices) of the human PFC used in this study were downloaded from the NCBI Gene Expression Omnibus database (GEO)1 under accession numbers GSE157827 (Lau et al., 2020 (link)), GSE174367 (Morabito et al., 2021 (link)), containing 24 AD and 17 control samples in total. After merging all the datasets, cells with less than 200 unique molecular identifiers (UMIs), more than 5,000 UMIs, or mitochondrial counts greater than 20% were filtered out. Genes expressed in fewer than three cells were also filtered out. Seurat (version 4.0)2 was used for a wide variety of single-cell analyses, including normalization, scaling, batch correction, dimensionality reduction, clustering, and visualization. We used the harmony package3 for the batch correction. The expression of known marker genes in the CNS was used as a reference for the annotation of different cell types. All statistical analyses were conducted using the R software (version 4.0.2).
Fan L.Y., Yang J., Li M.L., Liu R.Y., Kong Y., Duan S.Y., Guo G.Y., Yang J.H, & Xu Y.M. (2023). Single-nucleus transcriptional profiling uncovers the reprogrammed metabolism of astrocytes in Alzheimer’s disease. Frontiers in Molecular Neuroscience, 16, 1136398.
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Publication 2023
Cells Gene Expression Genes Homo sapiens Mitochondrial Inheritance Single-Cell Analysis Small Nuclear RNA

Top products related to «Single-Cell Analysis»

1
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Facscalibur by BD
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The FACSCalibur is a flow cytometry system designed for multi-parameter analysis of cells and other particles. It features a blue (488 nm) and a red (635 nm) laser for excitation of fluorescent dyes. The instrument is capable of detecting forward scatter, side scatter, and up to four fluorescent parameters simultaneously.
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The NovaSeq 6000 is a high-throughput sequencing system designed for large-scale genomic projects. It utilizes Illumina's sequencing by synthesis (SBS) technology to generate high-quality sequencing data. The NovaSeq 6000 can process multiple samples simultaneously and is capable of producing up to 6 Tb of data per run, making it suitable for a wide range of applications, including whole-genome sequencing, exome sequencing, and RNA sequencing.
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More about "Single-Cell Analysis"

Single-Cell Analysis (SCA) is a transformative technique that enables researchers to study individual cells within a heterogeneous population.
This powerful approach provides a deep, nuanced understanding of cellular diversity, function, and signaling pathways.
SCA leverages advanced technologies, such as flow cytometry (e.g., BD Rhapsody, FACSCalibur, FACSCanto II, CytoFLEX), microscopy, and next-generation sequencing (e.g., NovaSeq 6000), to analyze the unique properties of individual cells, including their gene expression, epigenetic profiles, and metabolic activities.
By focusing on the individual cell, researchers can uncover rare cell types, detect subtle cellular changes, and gain insights into complex biological processes that were previously inaccessiblr.
SCA has become an invaluable tool in fields like immunology, oncology, neuroscience, and developmental biology, allowing for the identification of novel cell types, the elucidation of disease mechanisms, and the development of targeted therapies.
With its growing importance in biomedical research, SCA continues to push the boundaries of our understanding of the fundamental unit of life – the single cell.
Researchers can leverage cutting-edge AI platforms like PubCompare.ai to optimize their SCA workflows, easily locate protocols from literature, pre-prints, and patents, and identify the best protocols and products for their research.
Tools like MATLAB and Prism 8 can also be used for data analysis and visualization, while DNase I can be employed for cell dissociation.
As SCA technology evolves, it promises to unlock new discoveries and revolutionize our understanding of the cellular universe, ultimately leading to advancements in diagnostic, therapeutic, and regenerative medicine.