Giulia Moro

BD Rhapsody is a widely used single-cell RNA-seq (scRNA-seq) platform to profile gene expression. Based on barcoded beads, processing BD Rhapsody experiments is demanding because of the multiple barcode versions and layouts, which makes it difficult to locate barcodes by scanning reads positionally. We introduce rhapsodist , a reproducible and scalable Snakemake workflow to process BD Rhapsody whole transcriptome analysis (WTA) data from raw FASTQ reads. As a result, rhapsodist generates count tables in a HDF5-backed SingleCellExperiment format. It supports all generations of BD Rhapsody beads, regardless of the barcode allowlist version and the presence of a variable-length diversity inset. rhapsodist orchestrates STARsolo, kallisto/bustools, salmon/alevin, and the official BD Rhapsody CWL pipeline, enabling direct cross-aligner comparison. Optionally, sample tag demultiplexing, read downsampling, tunable linker-mismatch tolerance, and per-sample reports are provided. We showcase a remarkable consistency across aligner results, and highlight their differences in performance (e.g., quantification, clustering, or speed). On simulated data all four aligners recovered true barcodes with perfect precision and recall and equivalent UMI counts. On HeLa cells (a cell line; enhanced beads), both per-cell correlations across aligners and pseudobulk correlations were remarkably high on every comparison (Pearson r of 0.89–0.96 and 0.94–0.98 per cell and pseudobulk, respectively). On a mouse epidermis dataset with a more complex transcriptional profile (e.g., with celltypes; on legacy beads), cell type pseudobulk correlations showed a similar consistency (Pearson r 0.92–0.98), pointing to calling robustness. As expected, pseudoaligners (kallisto/bustools and salmon/alevin) were best performers speed-wise, and BD’s workflow recovered a higher number of barcodes and UMIs. rhapsodist is available at https://github.com/imallona/rhapsodist under GPLv3, tried on Linux with conda, and includes unit and integration tests.

Current single-cell RNA sequencing (scRNA-seq) methods suffer from biases that restrict the detection of cellular transcripts to 10–40% of total RNAs. This hinders the identification of transcripts of interest. Additionally, information retrieved from most high-throughput scRNA-seq methods is confined to untranslated regions toward transcript ends, resulting in loss of detail in internal regions. In this review, we outline biases in scRNA-seq protocol steps that limit transcript and region detection. We then discuss the advantages and disadvantages of targeted sequencing solutions, grouped into five categories according to the protocol step they target. Finally, we present a decision tree that guides researchers in selecting the most suitable targeted method for their experiment.

Single-cell profiling technologies allow exploring molecular mechanisms that drive development, health, and disease. However, current methods still fall short of profiling single cell transcriptomes comprehensively, with one major challenge being high non-detection rates of specific transcripts and transcript regions. Such information is often crucial to understanding the biology of cells. Here, we introduce RoCK and ROI (Robust Capture of Key transcripts and Regions Of Interest), a scRNA-seq workflow encompassing two techniques. RoCKseq uses targeted capture to enrich for key transcripts, thereby supporting the detection and identification of cell types and complex phenotypes in scRNA-seq experiments. ROIseq directs a subset of reads to a specific region of interest via selective priming. Importantly, RoCK and ROI enables retrieval of specific sequence information without compromising overall single cell transcriptome information. We validate RoCK and ROI across diverse biological systems highlighting the versatility and showing the power of the method to retrieve critical transcriptomic features.

Reactivated embryonic programs are associated with cancer progression, yet their role and regulatory mechanisms remain poorly understood. In this study, we introduce ‘oncoembryology,’ an approach that systematically compares embryonic and cancerous tissues to identify shared molecular programs and assess their functional relevance in disease. Applying this strategy to colorectal cancer, we identified SoxC transcription factors (Sox4, Sox11, Sox12) as critical regulators of both embryonic development and tumorigenesis. SoxC transcription factors regulate diverse downstream targets, including Tead2, Mdk, and Klf4, thereby regulating crucial steps of colon development. Abrogating SoxC function in murine models reduced tumor growth and prevented liver metastasis. Concordantly, a SoxC-driven oncoembryonic gene signature correlated with poor survival in colorectal cancer patients, underscoring the therapeutic potential of targeting SoxC-regulated pathways in cancer treatment.

Computational biologists are frequently engaged in collaborative data analysis with wet lab researchers. These interdisciplinary projects, as necessary as they are to the scientific endeavor, can be surprisingly challenging due to cultural differences in operations and values. In this Ten Simple Rules guide, we aim to help dry lab researchers identify sources of friction and provide actionable tools to facilitate respectful, open, transparent, and rewarding collaborations.

Various tools have been developed to reliably identify, trace and analyze single cells in complex tissues. In recent years, these technologies have been combined with transcriptomic profiling approaches to explore molecular mechanisms that drive development, health, and disease. However, current methods still fall short of profiling single cell transcriptomes comprehensively, with one major challenge being high non-detection rates of specific transcripts and transcript regions. Such information is often crucial to understanding the biology of cells or tissues and includes lowly expressed transcripts, sequence variations and exon junctions. Here, we developed a scRNAseq workflow, RoCK and ROI (Robust Capture of Key transcripts and Regions Of Interest), that tackles these limitations. RoCKseq uses targeted capture to enrich for key transcripts, thereby supporting the detection and identification of cell types and complex phenotypes in scRNAseq experiments. ROIseq directs a subset of reads to a specific region of interest via selective priming to ensure detection. Importantly, RoCK and ROI guarantees efficient retrieval of specific sequence information without compromising overall single cell transcriptome information and our workflow is supported by a novel bioinformatics pipeline to analyze the multimodal information. RoCK and ROI represents a significant enhancement over non-targeted single cell sequencing, particularly when cell categorization depends on transcripts that are missed in standard scRNAseq experiments. In addition, it also allows exploration of biological questions that require assessment of specific sequence elements along the targets to be addressed.

The homeostasis of the gut epithelium relies upon continuous renewal and proliferation of crypt-resident intestinal epithelial stem cells (IESCs). Wnt/β-catenin signaling is required for IESC maintenance, however, it remains unclear how this pathway selectively governs the identity and proliferative decisions of IESCs. Here, we took advantage of knock-in mice harboring transgenic β-catenin alleles with mutations that specifically impair the recruitment of N- or C-terminal transcriptional co-factors. We show that C-terminally-recruited transcriptional co-factors of β-catenin act as all-or-nothing regulators of Wnt-target gene expression. Blocking their interactions with β-catenin rapidly induces loss of IESCs and intestinal homeostasis. Conversely, N-terminally recruited co-factors fine-tune β-catenin’s transcriptional output to ensure proper self-renewal and proliferative behaviour of IESCs. Impairment of N-terminal interactions triggers transient hyperproliferation of IESCs, eventually resulting in exhaustion of the self-renewing stem cell pool. IESC mis-differentiation, accompanied by unfolded protein response stress and immune infiltration, results in a process resembling aberrant “villisation” of intestinal crypts. Our data suggest that IESC-specific Wnt/β-catenin output requires selective modulation of gene expression by transcriptional co-factors.