SaVanT: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles

Lopez, David; Montoya, Dennis; Ambrose, Michael; Lam, Larry; Briscoe, Leah; Adams, Claire; Modlin, Robert L.; Pellegrini, Matteo

doi:10.1186/s12864-017-4167-7

Cited by 37 publications

(63 citation statements)

References 32 publications

(32 reference statements)

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“…TIGIT and CXCR6 respectively) ( Figure 3C-E and S4 ). Additionally, we cross-referenced each sub-cluster’s marker genes against a series of curated signatures in the Savant database (Lopez et al, 2017) to confirm our assignments. This analysis highlighted similarity to previously characterized T cell and NK cell populations ( Figure S4C ).…”

Section: Resultsmentioning

confidence: 99%

“…Using Seq-Well S^3, we were able to identify numerous myeloid cell subpopulations defined by a combinations of surface markers, cytokines and lineage-defining transcription factors. Specifically, we independently analyzed 2,371 myeloid cells and identified nine sub-clusters representing 4 primary myeloid cell types based on expression of canonical lineage markers and comparison to cell-type signatures in the Savant database: dendritic cells ( CLEC10A ), Langerhans cells ( CD207 and CD1A ), macrophages ( CD68 and CD163 ), and mast cells ( CPA3 and TPSAB1 ) ( Figure 4A and S4D-E ) (Lopez et al, 2017).…”

Section: Resultsmentioning

confidence: 99%

“…Notably, in alopecia, we report a sub-cluster of T cells characterized by expression of PDE4D ( Figure 3 ). PDE4 inhibitors have recently shown demonstrated efficacy in the treatment of alopecia (Keren et al, 2015; López et al, 2017), and it is intriguing to speculate that these inhibitors might work by targeting this subset of T cells.…”

Section: Discussionmentioning

confidence: 99%

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Highly Efficient, Massively-Parallel Single-Cell RNA-Seq Reveals Cellular States and Molecular Features of Human Skin Pathology

Tk¹,

Mh²,

Tm³

et al. 2019

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1The development of high-throughput single-cell RNA-sequencing (scRNA-Seq) methodologies 2 has empowered the characterization of complex biological samples by dramatically increasing the 3 number of constituent cells that can be examined concurrently. Nevertheless, these approaches 4 typically recover substantially less information per-cell as compared to lower-throughput microtiter 5 plate-based strategies. To uncover critical phenotypic differences among cells and effectively link 6 scRNA-Seq observations to legacy datasets, reliable detection of phenotype-defining transcripts 7 -such as transcription factors, affinity receptors, and signaling molecules -by these methods is 8 essential. Here, we describe a substantially improved massively-parallel scRNA-Seq protocol we 9 term Seq-Well S^3 ("Second-Strand Synthesis") that increases the efficiency of transcript capture 10 and gene detection by up to 10-and 5-fold, respectively, relative to previous iterations, surpassing 11 best-in-class commercial analogs. We first characterized the performance of Seq-Well S^3 in cell 12 lines and PBMCs, and then examined five different inflammatory skin diseases, illustrative of 13 distinct types of inflammation, to explore the breadth of potential immune and parenchymal cell 14 states. Our work presents an essential methodological advance as well as a valuable resource 15 for studying the cellular and molecular features that inform human skin inflammation. 109Mann-Whitney U Test & Linear Regression; Figure 1B-C). To confirm that these overall 110 improvements were not driven by changes in the relative frequencies of different cell types 111 captured by each technology, we also examined each subset independently (Figure S2A-B). For 112 each cell type detected, we observed significant increases in the numbers of transcripts captured 113 and genes detected using S^3 for each pairwise comparison between techniques (P < 0.05, 114 Mann-Whitney U Test; CD4 + T cells, Seq-Well V1: 1,044 ± 62.3 UMIs/cell; 10x v2: 7,671 ± 103.9 115 UMIs/cell; Seq-Well S^3: 13,390 ± 253.4 UMIs/cell; Mean ± Standard Error of the Median (SEM); 116 Figure S2). Both Seq-Well S^3 and 10x v2 displayed increased sensitivity for transcripts and 117 genes relative to Seq-Well v1, but Seq-Well S^3 showed the greatest efficiency (defined as genes 118 recovered at matched read depth) to detect genes for each cell type (Figure 1D-E; Figure S2). 119We sought to further understand whether these improvements resulted in enhanced 120 detection of biologically relevant genes typically under-represented in high-throughput single-cell 121 sequencing libraries (Tabula Muris Consortium et al., 2018). Importantly, genes that were 122 differentially detected (i.e., higher in S^3) within each cell type include numerous transcription 123 factors, cytokines and cell-surface receptors (Figure 1D-E). For example, among CD4 + T cells, 124we observe significantly increased detection of cytokines (e.g., TGFB1 and TNF), surface 125 receptors (e.g., TGFBR and CCR4) and transcription fact...

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Highly Efficient, Massively-Parallel Single-Cell RNA-Seq Reveals Cellular States and Molecular Features of Human Skin Pathology

Tk¹,

Mh²,

Tm³

et al. 2019

Preprint

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“…We hypothesized that the fraction of B cells in the sample will be proportional with the fraction of receptor-derived reads in our RNA-Seq data. We used a transcriptome-based computational method, SaVant 19 , which uses cell-specific gene signatures (independent of Ig transcripts) to infer the relative abundance of B cells within each tissue sample ( Table S5 ). The…”

Section: Feasibility Of Using Rna-seq To Study the Ig Receptor Repertmentioning

confidence: 99%

Profiling immunoglobulin repertoires across multiple human tissues by RNA Sequencing

Mangul

Mandric

Yang

et al. 2016

Preprint

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Assay-based approaches provide a detailed view of the adaptive immune system by profiling immunoglobulin (Ig) receptor repertoires. However, these methods carry a high cost and lack the scale of standard RNA sequencing (RNA-Seq). Here we report the development of ImReP, a novel computational method for rapid and accurate profiling of the immunoglobulin repertoire from regular RNA-Seq data. ImReP can also accurately assemble the complementary determining regions 3 (CDR3s), the most variable regions of Ig receptors. We applied our novel method to 8,555 samples across 53 tissues from 544 individuals in the Genotype-Tissue Expression (GTEx v6) project. ImReP is able to efficiently extract Ig-derived reads from RNA-Seq data. Using ImReP, we have created a systematic atlas of 3.6 million Ig sequences across a broad range of tissue types, most of which have not been studied for Ig receptor repertoires.We also compared the GTEx tissues to track the flow of Ig clonotypes across immune-related tissues, including secondary lymphoid organs and organs encompassing mucosal, exocrine, and endocrine sites, and we examined the compositional similarities of clonal populations between these tissues. The Atlas of Immunoglobulin Repertoires (The AIR), is freely available at https://github.com/smangul1/TheAIR/wiki , is one of the largest collection of CDR3 sequences and tissue types. We anticipate this recourse will enhance future immunology studies and advance the development of therapies for human diseases. ImReP is freely available at https://github.com/mandricigor/imrep/wiki Results Related workA number of tools have been developed to reconstruct the Ig receptor repertoire.Repertoire analysis from RNA-Seq data typically starts with mapping the reads to the germline V, D, and J genes obtained from the International ImMunoGeneTics (IMGT) database 12 . There are three possible read mapping scenarios: (1) the read is entirely mapped to the V gene; (2) the read is entirely mapped to the J gene; (3) the read is partially mapped to the V and J genes simultaneously. Existing methods consider only reads from category (3). These methods use different underlying algorithms to map reads to germline genes. For example, MiXCR 8 relies on an in-house alignment procedure, IgBlast 13 utilizes BLAST with an optimized set of parameters, and IMSEQ 14 uses in-house pairwise alignment between the read sequence and the germline V and J segment sequences.Following the alignment, MiXCR performs overlapping of previously aligned reads into contigs. The resulting contigs are re-aligned to the V, D, and J genes to verify that the significant portion of non-template N insertions is covered. In contrast to MiXCR, which simultaneously aligns reads to both V, D, and J genes, IgBlast separately aligns the query read to the databases comprised of V, D, and J genes. IgBlast uses a specific sequence to separately align; first, the program finds the best V gene hit. Then, IgBlast masks the aligned read region and performs an alignment to the J gene database. (In the ...

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“…Molecular signatures may also be utilized to inform biological interpretations. Molecular signatures are collections of genes with associated biological processes that can identify genes upregulated in specific sample subsets when compared to broader groups (15). Signatures can be composed of genes associated with specific diseases; for instance, breast cancer molecular signatures have identified subphenotypes indistinguishable by traditional histologic analysis (15, 16).…”

Section: Introductionmentioning

confidence: 99%

Molecular signatures for inflammation vary across cancer types and correlate significantly with tumor stage, gender and vital status of patients

Min

Lopez

et al. 2019

Preprint

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1516 Abstract 17 Cancer affects millions of individuals worldwide. One shortcoming of traditional cancer 18 classification systems is that, even for tumors affecting a single organ, there is significant 19 molecular heterogeneity. Precise molecular classification of tumors could be beneficial in 20 personalizing patients' therapy and predicting prognosis. To this end, here we propose to 21 use molecular signatures to further refine cancer classification. Molecular signatures are 22 collections of genes characterizing particular cell types, tissues or disease. Signatures can 23 be used to interpret expression profiles from heterogeneous samples. Large collections of 24 gene signatures have previously been cataloged in the MSigDB database. We have 25 developed a web-based Signature Visualization Tool (SaVanT) to display signature 26 scores in user-generated expression data. Here we have undertaken a systematic analysis 27 of correlations between inflammatory signatures and cancer samples, to test whether 28 inflammation can differentiate cancer types. Inflammatory response signatures were 29 obtained from MsigDB and SaVanT and a signature score was computed for samples 30 associated with 7 different cancer types. We first identified types of cancers that had 31 high inflammation levels as measured by these signatures. The correlation between 32 signature scores and metadata of these patients (gender, age at initial cancer diagnosis, 33 cancer stage, and vital status) was then computed. We sought to evaluate correlations 34 between inflammation with other clinical parameters and identified four cancer types that 35 had statistically significant association (p-value < 0.05) with at least one clinical 36 characteristic: pancreas adenocarcinoma (PAAD), cholangiocarcinoma (CHOL), kidney 37 chromophobe (KICH), and uveal melanoma (UVM). These results may allow future 3 38 studies to use these approaches to further refine cancer subtyping and ultimately 39 treatment.

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SaVanT: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles

Cited by 37 publications

References 32 publications

Highly Efficient, Massively-Parallel Single-Cell RNA-Seq Reveals Cellular States and Molecular Features of Human Skin Pathology

Highly Efficient, Massively-Parallel Single-Cell RNA-Seq Reveals Cellular States and Molecular Features of Human Skin Pathology

Profiling immunoglobulin repertoires across multiple human tissues by RNA Sequencing

Molecular signatures for inflammation vary across cancer types and correlate significantly with tumor stage, gender and vital status of patients

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