Joseph Lee

Spatial omics data are clustered to define both cell types and tissue domains. We present Building Aggregates with a Neighborhood Kernel and Spatial Yardstick (BANKSY), an algorithm that unifies these two spatial clustering problems by embedding cells in a product space of their own and the local neighborhood transcriptome, representing cell state and microenvironment, respectively. BANKSY’s spatial feature augmentation strategy improved performance on both tasks when tested on diverse RNA (imaging, sequencing) and protein (imaging) datasets. BANKSY revealed unexpected niche-dependent cell states in the mouse brain and outperformed competing methods on domain segmentation and cell typing benchmarks. BANKSY can also be used for quality control of spatial transcriptomics data and for spatially aware batch effect correction. Importantly, it is substantially faster and more scalable than existing methods, enabling the processing of millions of cell datasets. In summary, BANKSY provides an accurate, biologically motivated, scalable and versatile framework for analyzing spatially resolved omics data.

Most approaches to transcript quantification rely on fixed reference annotations; however, the transcriptome is dynamic and depending on the context, such static annotations contain inactive isoforms for some genes, whereas they are incomplete for others. Here we present Bambu, a method that performs machine-learning-based transcript discovery to enable quantification specific to the context of interest using long-read RNA-sequencing. To identify novel transcripts, Bambu estimates the novel discovery rate, which replaces arbitrary per-sample thresholds with a single, interpretable, precision-calibrated parameter. Bambu retains the full-length and unique read counts, enabling accurate quantification in presence of inactive isoforms. Compared to existing methods for transcript discovery, Bambu achieves greater precision without sacrificing sensitivity. We show that context-aware annotations improve quantification for both novel and known transcripts. We apply Bambu to quantify isoforms from repetitive HERVH-LTR7 retrotransposons in human embryonic stem cells, demonstrating the ability for context-specific transcript expression analysis.

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The ongoing COVID-19 pandemic is a clear and present threat to global public health. Research into how the causative SARS-CoV-2 virus together with its individual constituent genes and proteins interact with target host cells can facilitate the development of improved strategies to manage the acute and long-term complications of COVID-19. In this study, to better understand the biological roles of critical SARS-CoV-2 proteins, we determined and compared the host transcriptomic responses of the HL-CZ human pro-monocytic cell line upon transfection with key viral genes encoding the spike S1 subunit, S2 subunit, nucleocapsid protein (NP), NSP15 (endoribonuclease), and NSP16 (2′-O-ribose-methyltransferase). RNA sequencing followed by gene set enrichment analysis and other bioinformatics tools revealed that host genes associated with topologically incorrect protein, virus receptor activity, heat shock protein binding, endoplasmic reticulum stress, antigen processing and presentation were up-regulated in the presence of viral spike S1 expression. With spike S2 expression, pro-monocytic genes associated with the interferon-gamma-mediated signaling pathway, regulation of phosphatidylinositol 3-kinase activity, adipocytokine signaling pathway, and insulin signaling pathway were down-regulated, whereas those associated with cytokine-mediated signaling were up-regulated. The expression of NSP15 induced the up-regulation of genes associated with neutrophil degranulation, neutrophil-mediated immunity, oxidative phosphorylation, prion disease, and pathways of neurodegeneration. The expression of NSP16 resulted in the down-regulation of genes associated with S-adenosylmethionine-dependent methyltransferase activity. The expression of NP down-regulated genes associated with positive regulation of neurogenesis, nervous system development, and heart development. Taken together, the complex transcriptomic alterations arising from these viral-host gene interactions offer useful insights into host genes and their pathways that potentially contribute to SARS-CoV-2 pathogenesis.