scholarly journals BRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yuanhua Huang ◽  
Guido Sanguinetti

AbstractRNA splicing is an important driver of heterogeneity in single cells through the expression of alternative transcripts and as a determinant of transcriptional kinetics. However, the intrinsic coverage limitations of scRNA-seq technologies make it challenging to associate specific splicing events to cell-level phenotypes. BRIE2 is a scalable computational method that resolves these issues by regressing single-cell transcriptomic data against cell-level features. We show that BRIE2 effectively identifies differential disease-associated alternative splicing events and allows a principled selection of genes that capture heterogeneity in transcriptional kinetics and improve RNA velocity analyses, enabling the identification of splicing phenotypes associated with biological changes.

2020 ◽  
Author(s):  
Yuanhua Huang ◽  
Guido Sanguinetti

AbstractRNA splicing is an important driver of heterogeneity in single cells, and a major determinant of the dynamical state of developing cells. However, the intrinsic coverage limitations of scRNA-seq technologies make it challenging to associate specific splicing events to cell-level phenotypes. Here, we present BRIE2, a scalable computational method that resolves these issues by regressing single-cell transcriptomic data against cell-level features. We show on different biological systems that BRIE2 effectively identifies differential splicing events that are associated with disease or developmental lineages, and detects differential momentum genes for improving RNA velocity analyses. BRIE2 therefore extends the scope of single-cell transcriptomic experiments towards the identification of splicing phenotypes associated with biological changes at the single-cell level.


2020 ◽  
Vol 6 (36) ◽  
pp. eabb2754
Author(s):  
Lars Behrendt ◽  
M. Mehdi Salek ◽  
Erik L. Trampe ◽  
Vicente I. Fernandez ◽  
Kang Soo Lee ◽  
...  

Photosynthetic microorganisms are key players in aquatic ecosystems with strong potential for bioenergy production, yet their systematic selection at the single-cell level for improved productivity or stress resilience (“phenotyping”) has remained largely inaccessible. To facilitate the phenotyping of microalgae and cyanobacteria, we developed “PhenoChip,” a platform for the multiparametric photophysiological characterization and selection of unicellular phenotypes under user-controlled physicochemical conditions. We used PhenoChip to expose single cells of the coral symbiont Symbiodinium to thermal and chemical treatments and monitor single-cell photophysiology via chlorophyll fluorometry. This revealed strain-specific thermal sensitivity thresholds and distinct pH optima for photosynthetic performance, and permitted the identification of single cells with elevated resilience toward rising temperature. Optical expulsion technology was used to collect single cells from PhenoChip, and their propagation revealed indications of transgenerational preservation of photosynthetic phenotypes. PhenoChip represents a versatile platform for the phenotyping of photosynthetic unicells relevant to biotechnology, ecotoxicology, and assisted evolution.


Author(s):  
Carlos F. Buen Abad Najar ◽  
Nir Yosef ◽  
Liana F. Lareau

Single cell RNA sequencing provides powerful insight into the factors that determine each cell’s unique identity, including variation in transcription and RNA splicing among diverse cell types. Previous studies led to the surprising observation that alternative splicing outcomes among single cells are highly variable and follow a bimodal pattern: a given cell consistently produces either one or the other isoform for a particular splicing choice, with few cells producing both isoforms. Here we show that this pattern arises almost entirely from technical limitations. We analyzed single cell alternative splicing in human and mouse single cell RNA-seq datasets, and modeled them with a probablistic simulator. Our simulations show that low gene expression and low capture efficiency distort the observed distribution of isoforms in single cells. This gives the appearance of a binary isoform distribution, even when the underlying reality is consistent with more than one isoform per cell. We show that accounting for the true amount of information recovered can produce biologically meaningful measurements of splicing in single cells.


2021 ◽  
Vol 7 (8) ◽  
pp. eabe3610
Author(s):  
Conor J. Kearney ◽  
Stephin J. Vervoort ◽  
Kelly M. Ramsbottom ◽  
Izabela Todorovski ◽  
Emily J. Lelliott ◽  
...  

Multimodal single-cell RNA sequencing enables the precise mapping of transcriptional and phenotypic features of cellular differentiation states but does not allow for simultaneous integration of critical posttranslational modification data. Here, we describe SUrface-protein Glycan And RNA-seq (SUGAR-seq), a method that enables detection and analysis of N-linked glycosylation, extracellular epitopes, and the transcriptome at the single-cell level. Integrated SUGAR-seq and glycoproteome analysis identified tumor-infiltrating T cells with unique surface glycan properties that report their epigenetic and functional state.


Author(s):  
Tianming Zhou ◽  
Ruochi Zhang ◽  
Jian Ma

The spatial organization of the genome in the cell nucleus is pivotal to cell function. However, how the 3D genome organization and its dynamics influence cellular phenotypes remains poorly understood. The very recent development of single-cell technologies for probing the 3D genome, especially single-cell Hi-C (scHi-C), has ushered in a new era of unveiling cell-to-cell variability of 3D genome features at an unprecedented resolution. Here, we review recent developments in computational approaches to the analysis of scHi-C, including data processing, dimensionality reduction, imputation for enhancing data quality, and the revealing of 3D genome features at single-cell resolution. While much progress has been made in computational method development to analyze single-cell 3D genomes, substantial future work is needed to improve data interpretation and multimodal data integration, which are critical to reveal fundamental connections between genome structure and function among heterogeneous cell populations in various biological contexts. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.


Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2920-2920
Author(s):  
Marianna Romzova ◽  
Dagmar Smitalova ◽  
Peter Taus ◽  
Jiri Mayer ◽  
Martin Culen

BACKGROUND: Bcr-abl1 oncogene targeted treatment with tyrosine kinase inhibitors (TKI) showed an impressive efficacy against proliferating chronic myeloid leukemia (CML) cells. However, rapid relapses in more than half of CML patients after discontinuation of the treatment suggest a presence of quiescent leukemic stem cells inherently resistant to BCR-ABL1 inhibition. Understanding the heterogeneity of CML stem cell compartment is crucial for preventing the treatment failure. Specificity of already established leukemic stem cell (LSC) markers has been tested mainly in bulk CD34+CD38- populations at diagnosis. Phenotypes and molecular signatures of therapy resistant BCR ABL1 positive stem cells is however yet to be established. AIMS: Identification of BCR-ABL1 dependent LSC markers at single cell level by direct comparison their surface and transcript expression with the levels and the presence of BCR-ABL1 transcript at diagnosis and after administration of TKI treatment. METHODS: Total number of 375 cells were obtained from bone marrow and peripheral blood of 4 chronic phase CML patients. Cells were collected prior any treatment and three months after TKI treatment initiation. Normal bone marrow cells and BCR-ABL1 positive K562 cell line were used as controls. Indexed immuno-phenotyping and sorting of CD34+CD38- single cells was performed using a panel of 11 specific surface markers. Collected single cells were lysed and cDNA was enriched for 11 targets using 22 cycle pre-amplification. Expression profiling was carried on SmartChip real-time PCR system (Takara Bio) detecting following genes: BCR-ABL1, CD26, CD25, IL1-Rap, CD56, CD90, CD93, CD69, KI67, and control genes GUS and HPRT. Unsupervised clustering was performed using principal component analysis (PCA). Correlations were measured by Spearman rank method. RESULTS: At diagnosis, majority of BCR-ABL1+ C34+CD38- stem cells co-express IL1-Rap, CD26, and CD69 on their surface (88%, 82%, 78% overlap). Only 56% of BCR-ABL1+ cells positive for aforementioned markers co-express CD25, 28% CD93 and 16% CD56. The expression of these markers could also be detected in 4-11% of BCR-ABL1- cell, although this could be technical inaccuracy caused by the single cell profiling. CD90 marker did not show any correlation with BCR-ABL1 expression. At transcript level the expression of IL-1Rap, CD26, CD25 and CD56 was observed in 62%, 52% 45% and 16% BCR-ABL1+ cells, and up to 7% of BCR-ABL1- cells. CD69 expression was observed in 90% of BCR-ABL+ cells at transcript level, but also in 71% BCR-ABL- cells. BCR-ABL1 independent expression was observed for cKIT. (60% vs. 76 % in positive vs negative). Finally proliferation marker KI67 was expressed only in 6% of the BCR-ABL1+ cells. PCA analysis divided cells into several distinct clusters with BCR-ABL1 as the main contributor, and cKIT, CD69 and CD26, IL-1RAP as other significant factors. Interestingly BCR-ABL1+ cells collected during TKI treatment showed persistent surface expression of IL-1Rap and CD26, while CD56, CD69 and CD93 were only on part of the BCR-ABL1+ cells. CD25 was significantly deregulated during TKI treatment. CONCLUSION: At diagnosis up to 80% of LSC co-express 3 specific surface markers - IL-1RAP, CD26 and CD69. Variable portion of LSC co-express additional markers such are CD25, CD56 and CD93. During TKI treatment the surface expression of majority of markers is decreased, where the best correlated LSC marker is IL-1Rap, followed by CD26 and CD69. CD56 marker seems to persist in the same proportion of cells while CD25 disappears. cKIT is highly expressed in normal BM and HSC from CML patients, but also in some LSC. CD34+CD38- cells show non-proliferating phenotype. Disclosures Mayer: AOP Orphan Pharmaceuticals AG: Research Funding.


Cells ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 285
Author(s):  
Eszter Széles ◽  
Krisztina Nagy ◽  
Ágnes Ábrahám ◽  
Sándor Kovács ◽  
Anna Podmaniczki ◽  
...  

Chlamydomonas reinhardtii is a model organism of increasing biotechnological importance, yet, the evaluation of its life cycle processes and photosynthesis on a single-cell level is largely unresolved. To facilitate the study of the relationship between morphology and photochemistry, we established microfluidics in combination with chlorophyll a fluorescence induction measurements. We developed two types of microfluidic platforms for single-cell investigations: (i) The traps of the “Tulip” device are suitable for capturing and immobilizing single cells, enabling the assessment of their photosynthesis for several hours without binding to a solid support surface. Using this “Tulip” platform, we performed high-quality non-photochemical quenching measurements and confirmed our earlier results on bulk cultures that non-photochemical quenching is higher in ascorbate-deficient mutants (Crvtc2-1) than in the wild-type. (ii) The traps of the “Pot” device were designed for capturing single cells and allowing the growth of the daughter cells within the traps. Using our most performant “Pot” device, we could demonstrate that the FV/FM parameter, an indicator of photosynthetic efficiency, varies considerably during the cell cycle. Our microfluidic devices, therefore, represent versatile platforms for the simultaneous morphological and photosynthetic investigations of C. reinhardtii on a single-cell level.


2020 ◽  
Author(s):  
Shang Liu ◽  
Biaofeng Zhou ◽  
Liang Wu ◽  
Yan Sun ◽  
Jie Chen ◽  
...  

Abstract Recent advances in single-cell RNA sequencing (scRNA-seq) have improved our understanding of the association between tumor-infiltrating lymphocyte (TILs) heterogeneity and cancer initiation and progression. However, studies investigating alternative splicing (AS) as an important regulatory factor of heterogeneity remain limited. Here, we developed a new computational tool, DESJ-detection, which accurately detects differentially expressed splicing junctions (DESJs) between cell groups at the single-cell level. We analyzed 5,063 T cells of hepatocellular carcinoma (HCC) and identified 1,176 DESJs across 11 T cell subtypes. Interestingly, DESJs were enriched in UTRs, and have putative effects on heterogeneity. Cell subtypes with a similar function closely clustered together at the AS level. Meanwhile, we identified two novel cell states, pre-exhaustion and pre-activation with the isoform markers CD103-201 and ARHGAP15-205. In summary, we present a comprehensive investigation of alternative splicing differences, which provided novel insights into T cell heterogeneity and can be applied to other full-length scRNA-seq datasets.


2021 ◽  
Author(s):  
Hyobin Jeong ◽  
Karen Grimes ◽  
Peter-Martin Bruch ◽  
Tobias Rausch ◽  
Patrick Hasenfeld ◽  
...  

Somatic structural variants (SVs) are widespread in cancer genomes, however, their impact on tumorigenesis and intra-tumour heterogeneity is incompletely understood, since methods to functionally characterize the broad spectrum of SVs arising in cancerous single-cells are lacking. We present a computational method, scNOVA, that couples SV discovery with nucleosome occupancy analysis by haplotype-resolved single-cell sequencing, to systematically uncover SV effects on cis-regulatory elements and gene activity. Application to leukemias and cell lines uncovered SV outcomes at several loci, including dysregulated cancer-related pathways and mono-allelic oncogene expression near SV breakpoints. At the intra-patient level, we identified different yet overlapping subclonal SVs that converge on aberrant Wnt signaling. We also deconvoluted the effects of catastrophic chromosomal rearrangements resulting in oncogenic transcription factor dysregulation. scNOVA directly links SVs to their functional consequences, opening the door for single-cell multiomics of SVs in heterogeneous cell populations.


2018 ◽  
Author(s):  
Douglas Abrams ◽  
Parveen Kumar ◽  
R. Krishna Murthy Karuturi ◽  
Joshy George

AbstractBackgroundThe advent of single cell RNA sequencing (scRNA-seq) enabled researchers to study transcriptomic activity within individual cells and identify inherent cell types in the sample. Although numerous computational tools have been developed to analyze single cell transcriptomes, there are no published studies and analytical packages available to guide experimental design and to devise suitable analysis procedure for cell type identification.ResultsWe have developed an empirical methodology to address this important gap in single cell experimental design and analysis into an easy-to-use tool called SCEED (Single Cell Empirical Experimental Design and analysis). With SCEED, user can choose a variety of combinations of tools for analysis, conduct performance analysis of analytical procedures and choose the best procedure, and estimate sample size (number of cells to be profiled) required for a given analytical procedure at varying levels of cell type rarity and other experimental parameters. Using SCEED, we examined 3 single cell algorithms using 48 simulated single cell datasets that were generated for varying number of cell types and their proportions, number of genes expressed per cell, number of marker genes and their fold change, and number of single cells successfully profiled in the experiment.ConclusionsBased on our study, we found that when marker genes are expressed at fold change of 4 or more than the rest of the genes, either Seurat or Simlr algorithm can be used to analyze single cell dataset for any number of single cells isolated (minimum 1000 single cells were tested). However, when marker genes are expected to be only up to fC 2 upregulated, choice of the single cell algorithm is dependent on the number of single cells isolated and proportion of rare cell type to be identified. In conclusion, our work allows the assessment of various single cell methods and also aids in examining the single cell experimental design.


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