Biology subjects

Fontanez, K.

Publications and source records attributed to Fontanez, K..

2 recordsLinked to original sources

bioRxiv2024

Intrinsic molecular identifiers enable robust molecular counting in single-cell sequencing

Particle-templated instant partition sequencing (PIPseq), is an emerging approach for massively scalable single-cell gene expression studies that does not require complex instrumentation or expensive consumables. We present PIPseqTM V, a novel implementation of the PIPseq workflow with significant improvements in assay performance and sensitivity compared to prior methods. Among the innovations driving the improved performance in PIPseq V is a new approach for transcript counting using Intrinsic Molecular Identifiers (IMIs) from the captured transcript sequence, eliminating the need for traditional Unique Molecular Identifiers (UMIs). IMIs are the starting positions for molecules generated by random fragmentation after limited cycle PCR, which can be used to uniquely identify individual transcript copies of genes expressed within individual cells. To correct for experimental variation in IMI generation, we have developed a dynamic correction strategy that can adapt to different sample cell input, transcript expression, and sequencing depth without the need for external benchmarking or controls. Our results demonstrate that PIPseq V with IMI-based analysis provides biological information comparable to established UMI-based approaches while avoiding UMI-associated biases. Dynamic correction offers a robust and data-driven analysis strategy for scRNAseq.

genomics↗

bioRxiv2023

Single-cell analysis of the nervous system at small and large scales with instant partitions

Single-cell RNA sequencing is a new frontier across all biology, particularly in neuroscience. While powerful for answering numerous neuroscience questions, limitations in sample input size, and initial capital outlay can exclude some researchers from its application. Here, we tested a recently introduced method for scRNAseq across diverse scales and neuroscience experiments. We benchmarked against a major current scRNAseq technology and found that PIPseq performed similarly, in line with earlier benchmarking data. Across dozens of samples, PIPseq recovered many brain cell types at small and large scales (1,000-100,000 cells/sample) and was able to detect differentially expressed genes in an inflammation paradigm. Similarly, PIPseq could detect expected and new differentially expressed genes in a brain single cell suspension from a knockout mouse model; it could also detect rare, virally-la-belled cells following lentiviral targeting and gene knockdown. Finally, we used PIPseq to investigate gene expression in a nontraditional model species, the little skate (Leucoraja erinacea). In total, PIPSeq was able to detect single-cell gene expression changes across models and species, with an added benefit of large scale capture and sequencing of each sample.

neuroscience↗