Biology subjects

Shanthikumar, S.

Publications and source records attributed to Shanthikumar, S..

8 recordsLinked to original sources

bioRxiv2026

Order Matters: Ambient RNA correction confounds doublet detection in single-cell RNA sequencing

A common technical artifact in droplet-based single-cell RNA sequencing (scRNA-seq) is the capture of two cells in the same droplet, termed doublets. Doublets may present as hybrid cell types, and their removal is a critical step during quality control. Another form of contamination is ambient RNA, which originates from free floating RNA released from other cells in suspension, likely during cell death. As with doublets, ambient RNA results in unexpected gene expression patterns. Most suggested analysis pipelines include ambient RNA correction before doublet detection however no formal investigation into how doublet detection is affected by ambient RNA correction has been conducted. Here, we evaluate the impact of ambient RNA correction on the performance of doublet detection using novel engineered and multiplexed datasets. We apply three of the most widely-used methods for removing ambient RNA (decontX, soupX, CellBender), in conjunction with up to six doublet detection methods (scDblFinder, scds, bcds, cxds, scrublet, DoubletDetection). We find that doublet detection tools perform best when done prior to ambient RNA correction, with the exception of CellBender. Performing ambient RNA correction first, particularly for decontX, leads to an increased proportion of doublet droplets retained in the dataset compared with calling doublets on uncorrected data. We show that both decontX and soupX partially mask doublet signal by removing proportionally more ambient RNA from doublets that remain undetected after ambient RNA correction. Our results show that the performance of doublet detection is significantly impacted by the use of ambient RNA correction tools. We suggest a new workflow that implements doublet detection prior to ambient RNA correction unless using CellBender.

bioinformatics↗

bioRxiv2026

Single-cell CRISPR activation screens in primary B cells discover gene regulatory mechanisms for hundreds of autoimmune risk loci.

Genome-wide association studies (GWAS) have discovered thousands of genetic variants linked to autoimmune disease, and yet the molecular pathways underlying autoimmunity have remained elusive. A key challenge is that >90% of identified GWAS risk loci are in non-coding genomic regions making it difficult to predict their relevance to disease. Here, we have curated fine-mapped non-coding risk variants from over 30 different autoimmune traits including common conditions such as systemic lupus erythematosus (SLE), Crohns disease, and multiple sclerosis, and reveal shared genetic signatures between diverse autoimmune diseases. We subsequently performed a high-throughput single-cell multi-omic CRISPR activation screen targeting 763 autoimmune risk loci in primary human B cells (a highly relevant cell type to autoimmune diseases) and discover 524 cis-regulatory target gene effects for 378 risk loci, with many risk loci regulating multiple gene targets. This Single Cell Analysis of Non-coding Distal Autoimmune Loci (SCANDAL) provides a powerful experimental resource linking non-coding risk loci to many disease-relevant genes, including lowly-expressed cytokines and transcription factors for which perturbation effects can be difficult to quantify with other CRISPR-based strategies. We reveal how increased transcriptional activity at one non-coding risk locus can drive transcription at other risk loci within the same regulatory landscape that may be relevant to understand genetic pleiotropy of autoimmune diseases. Finally, we quantified allele-specific effects on target gene expression with massive parallel reporter assays and prime editing to discover a gain-of-function variant associated with SLE that controls expression of the transcription factor REL/cREL which subsequently binds dozens of risk loci and target genes associated with different autoimmune diseases. Our study provides a valuable resource linking non-coding risk loci with their cis-regulatory target genes and advances our understanding of the shared genetic networks and mechanisms involved in autoimmunity.

genomics↗

bioRxiv2025

Macrophages Mediate Antiviral Immunity and Repair of Type 2 Alveolar Epithelial Cells in a Human Stem Cell Model

The lung alveoli are constantly exposed to inhaled pathogens and inorganic hazards, relying on robust defence mechanisms to maintain homeostasis. Alveolar macrophages and type 2 alveolar epithelial cells (AT2s) collaborate to orchestrate protection. Compromised defence can dysregulate immunity and repair, leading to acute and chronic respiratory diseases. To better understand these processes and drive therapeutic discovery, human model systems that capture key cell interactions are essential. Here, we develop the first induced pluripotent stem cell (iPSC)-derived platform that integrates AT2 cells and macrophages in an air-liquid interface culture. Coculture enhanced AT2-specific gene expression and lipid synthesis, while macrophages actively phagocytosed AT2-derived surfactant. iPSC-derived AT2s supported macrophage survival by producing M-CSF and coculture promoted an alveolar macrophage-like phenotype. Additionally, during respiratory infection macrophages played a crucial role in modulating proinflammatory signalling, enhancing antiviral immunity, and restricting viral replication. Furthermore, we identify a role for iPSC-derived macrophages in epithelial repair, with VEGF signalling to macrophages increasing epithelial permeability. We present an iPSC-derived air-interface platform to study AT2-macrophage interactions in homeostasis, infection, and repair, providing insights into their potential roles in the initiation and progression of respiratory diseases.

cell biology↗

bioRxiv2025

Antibody responses against bacterial glycans affinity mature and diversify in germinal centers.

Anti-carbohydrate antibodies (Abs) play crucial roles in pathogen control, but their generation remains poorly understood. By studying responses to Streptococcus pyogenes in humans, we reveal that the glycan-targeted response shifts from IgM towards IgG and IgA memory with age and antigen exposure across blood, spleen, and tonsils. Both natural colonization and controlled human infection with S. pyogenes increased class-switched B cells, with evidence of within-clone switching. Glycan-specific B cells readily participated in germinal center (GC) responses and showed robust somatic hypermutation despite a molecular signature consistent with receiving reduced T cell help. We conclude that mucosal pathogen encounters elicit glycan responses that class-switch, evolve and diversify through the GC. These findings reveal how age and infection history can influence the quality, quantity, and isotype use of glycan-specific B cells, with implications for the design and schedule of glycan-containing vaccines.

immunology↗

bioRxiv2024

More cells, more doublets in highly multiplexed single-cell data

Withdrawal statementThe authors have withdrawn this manuscript. In our initial submission of this paper, we proposed a combinatoric model for the probability of finding doublets in sample-barcoded single-cell RNA-sequencing data. This model predicts a doublet rate higher than the rate included in the documentation for the 10X Flex protocol. This was motivated by our use of doublet-finding software, scDblFinder, on experimental data using the Flex protocol, which identified several times more doublets than we expected based on the documentation. Our original model produced better agreement with the results of scDblFinder in 9 of our own data sets as well as one public dataset made available by 10X. During revisions, however, we performed Monte Carlo simulations of doublet formation that were inconsistent with the results of our model, and much closer to the predictions from the documentation. This prompted us to reanalyse the assumptions of our model. A longer version of this withdrawal statement that explains in detail the errors in the model and includes updated figures is available at https://github.com/Oshlack/flex-doublets, but in brief, our initial assumptions over-predicted the doublet fraction, and our revised model is now consistent with both the Monte Carlo simulations and the predictions from the Flex documentation. We therefore speculate that the unexpectedly high doublet fractions observed in the experimental data might be due to some combination of experimental conditions and software performance. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please ontact the corresponding author.

bioinformatics↗

bioRxiv2022

Benchmarking single-cell hashtag oligo demultiplexing methods

Sample multiplexing is often used to reduce cost and limit batch effects in single-cell RNA sequencing (scRNA-seq) experiments. A commonly used multiplexing technique involves tagging cells prior to pooling with a hashtag oligo (HTO) that can be sequenced along with the cells RNA to determine their sample of origin. Several tools have been developed to demultiplex HTO sequencing data and assign cells to samples. In this study, we critically assess the performance of seven HTO demultiplexing tools: hashedDrops, HTODemux, GMM-Demux, demuxmix, deMULTIplex, BFF and HashSolo. The comparison uses data sets where each sample has also been demultiplexed using genetic variants from the RNA, enabling comparison of HTO demultiplexing techniques against complementary data from the genetic "ground truth". We find that all methods perform similarly where HTO labelling is of high quality, but methods that assume a bimodal counts distribution perform poorly on lower quality data. We also suggest heuristic approaches for assessing the quality of HTO counts in a scRNA-seq experiment.

bioinformatics↗

bioRxiv2022

Multimodal single cell analysis of the paediatric lower airway reveals novel immune cell phenotypes in early life health and disease

Inflammation is a key driver of cystic fibrosis (CF) lung disease, not addressed by current standard care. Improved understanding of the mechanisms leading to aberrant inflammation may assist the development of effective anti-inflammatory therapy. Single-cell RNA sequencing (scRNA-seq) allows profiling of cell composition and function at previously unprecedented resolution. Herein, we seek to use multimodal single-cell analysis to comprehensively define immune cell phenotypes, proportions and functional characteristics in preschool children with CF. We analyzed 42,658 cells from bronchoalveolar lavage of 11 preschool children with CF and a healthy control using scRNA-seq and parallel assessment of 154 cell surface proteins. Validation of cell types identified by scRNA-seq was achieved by assessment of samples by spectral flow cytometry. Analysis of transcriptome expression and cell surface protein expression, combined with functional pathway analysis, revealed 41 immune and epithelial cell populations in BAL. Spectral flow cytometry analysis of over 256,000 cells from a subset of the same patients revealed high correlation in major cell type proportions across the two technologies. Macrophages consisted of 13 functionally distinct sub populations, including previously undescribed populations enriched for markers of vesicle production and regulatory/repair functions. Other novel cell populations included CD4 T cells expressing inflammatory IFN/{beta} and NF{kappa}B signalling genes. Our work provides a comprehensive cellular analysis of the pediatric lower airway in preschool children with CF, reveals novel cell types and provides a reference for investigation of inflammation in early life CF.

systems biology↗

bioRxiv2021

Application of high dimensional flow cytometry and unsupervised analysis to define the immune cell landscape of early childhood respiratory and blood compartments

The cellular landscape of the paediatric respiratory system remains largely uncharacterised and as a result, the mechanisms of highly prevalent childhood respiratory diseases remain poorly understood. A major limitation in defining mechanisms of disease has been the availability of tissue samples collected in early life, as well as technologies that permit deep immune analysis from limited sample volumes. In this work, we developed new experimental methods and applied unsupervised analytical tools to profile the local (bronchoalveolar lavage) and systemic (whole blood) immune response in childhood respiratory disease. We quantified and comprehensively phenotyped immune cell populations across blood and lung compartments in young children (under 6 years of age), showed that inflammatory cells in the BAL express higher levels of activation and migration markers relative to their systemic counterparts, and applied new analytical tools to reveal novel tissue-resident macrophage and infiltrating monocyte populations in the paediatric lung. To our knowledge, this is the first description of the use of these methods for paediatric respiratory samples. Combined with matched analysis of the systemic immune cell profile, the application of these pipelines will increase our understanding of childhood lung disease with potential to identify clinically relevant disease biomarkers.

immunology↗