Sablauskas, K. | bioRxiv Science

bioRxiv2025

High-throughput single cell -omics using semi-permeable capsules

Biological systems are inherently complex and heterogeneous. Deciphering this complexity increasingly relies on high-throughput analytical methods and tools that efficiently probe the cellular phenotype and genotype. While recent advancements have enabled various single-cell -omics assays, their broader applications are inherently limited by the challenge of efficiently conducting multi-step biochemical assays while retaining various biological analytes. Extending on our previous work (1) here we present a versatile technology based on semi-permeable capsules (SPCs), tailored for a variety of high-throughput nucleic acid assays, including digital PCR, genome sequencing, single-cell RNA-sequencing (scRNA-Seq) and FACS-based isolation of individual transcriptomes based on nucleic acid marker of interest. Being biocompatible, the SPCs support single-cell cultivation and clonal expansion over long periods of time - a fundamental limitation of droplet microfluidics systems. Using SPCs we perform scRNA-Seq on white blood cells from patients with hematopoietic disorders and demonstrate that capsule-based sequencing approach (CapSeq) offers superior transcript capture, even for the most challenging cell types. By applying CapSeq on acute myeloid leukemia (AML) samples, we uncover notable changes in transcriptomes of mature granulocytes and monocytes associated with blast and progenitor cell phenotypes. Accurate representation of the entirety of the cellular heterogeneity of clinical samples, driving new insights into the malfunctioning of the innate immune system, and ability to clonally expand individual cells over long periods of time, positions SPC technology as customizable, highly sensitive and broadly applicable tool for easy-to-use, scalable single-cell -omics applications.

genomics↗

bioRxiv2021

DeNovoCNN: A deep learning approach to de novo variant calling in next generation sequencing data

De novo mutations (DNMs) are an important cause of genetic disorders. The accurate identification of DNMs from sequencing data is therefore fundamental to rare disease research and diagnostics. Unfortunately, identifying reliable DNMs remains a major challenge due to sequence errors, uneven coverage, and mapping artifacts. Here, we developed a deep convolutional neural network (CNN) DNM caller (DeNovoCNN), that encodes the alignment of sequence reads for a trio as 160x164 resolution images. DeNovoCNN was trained on DNMs of 5,616 whole exome sequencing (WES) trios achieving total 96.74% recall and 96.55% precision on the test dataset. We find that DeNovoCNN has increased recall/sensitivity and precision compared to existing DNM calling approaches (GATK, DeNovoGear, DeepTrio, Samtools) based on the Genome in a Bottle reference dataset and independent WES and WGS trios. Validations of DNMs based on Sanger and PacBio HiFi sequencing confirm that DeNovoCNN outperforms existing methods. Most importantly, our results suggest that DeNovoCNN is likely robust against different exome sequencing and analyses approaches, thereby allowing the application on other datasets. DeNovoCNN is freely available as a Docker container and can be run on existing alignment (BAM/CRAM) and variant calling (VCF) files from WES and WGS without a need for variant recalling.

bioinformatics↗