Biology subjects

Widman, A. J.

Publications and source records attributed to Widman, A. J..

5 recordsLinked to original sources

bioRxiv2025

Paired plus-minus sequencing is an ultra-high throughput and accurate method for dual strand sequencing of DNA molecules

Distinguishing real biological variation in the form of single-nucleotide variants (SNVs) from errors is a major challenge for genome sequencing technologies. This is particularly true in settings where SNVs are at low frequency such as cancer detection through liquid biopsy, or human somatic mosaicism. State-of-the-art molecular denoising approaches for DNA sequencing rely on duplex sequencing, where both strands of a single DNA molecule are sequenced to discern true variants from errors arising from single stranded DNA damage. However, such duplex approaches typically require massive over-sequencing to overcome low capture rates of duplex molecules. To address these challenges, we introduce paired plus-minus sequencing (ppmSeq) technology, in which both DNA strands are partitioned and clonally amplified on sequencing beads through emulsion PCR. In this reaction, both strands of a double-stranded DNA molecule contribute to a single sequencing read, allowing for a duplex yield that scales linearly with sequencing coverage across a wide range of inputs (1.8-98 ng). We benchmarked ppmSeq against current duplex sequencing technologies, demonstrating superior duplex recovery with ppmSeq, with a rate of 44%{+/-}5.5% (compared to [~]5-11% for leading duplex technologies). Using both genomic as well as cell-free DNA, we established error rates for ppmSeq, which had residual SNV detection error rates as low as 7.98x10-8 for gDNA (using an end-repair protocol with dideoxy nucleotides) and 3.5x10-7{+/-}7.5x10-8 for cell-free DNA. To test the capabilities of ppmSeq for error-corrected whole-genome sequencing (WGS) for clinical application, we assessed circulating tumor DNA (ctDNA) detection for disease monitoring in cancer patients. We demonstrated that ppmSeq enables powerful tumor-informed ctDNA detection at concentrations of 10-4 across most cancers, parts per million sensitivity in cancers with high mutation burden, and further increased sensitivity with higher sequencing depth. We then leveraged genome-wide trinucleotide mutation patterns characteristic of urothelial (APOBEC3-related and platinum exposure-related signatures) and lung (tobacco-exposure-related signatures) cancers to perform tumor-naive ctDNA detection, showing that ppmSeq can identify a disease-specific signal in plasma cell-free DNA without a matched tumor, and that this signal correlates with imaging-based disease metrics. Altogether, ppmSeq provides an error-corrected, cost-efficient and scalable approach for high-fidelity WGS that can be harnessed for challenging clinical applications and emerging frontiers in human somatic genetics where high accuracy is required for mutation identification.

genomics↗

bioRxiv2025

Human sensory neurons exhibit cell-type-specific, pain-associated differences in intrinsic excitability and expression of SCN9A and SCN10A

Despite major advances in pain science, the approval of novel therapeutics has been slow. A major cause for the lack of new analgesics may be fundamental biological differences between humans and model organisms used in preclinical research. Large-scale transcriptional profiling efforts on human dorsal root ganglia (hDRG) have now identified at least 22 distinct neuronal subtypes; however, a significant knowledge gap exists in ascribing functional phenotypes to these diverse neuronal populations. In this study, we use Patch-seq recordings in hDRG to link electrical properties to transcriptionally defined cell types. First, through unbiased clustering of electrophysiological properties from 228 hDRG neurons, we identify three electrophysiological subtypes (E-types). Next, we show that E-types can be mapped onto specific transcriptional classes (T-types) of hDRG neurons. We find that donors pain history is associated with E-type-specific differences in electrical properties, some of which may be associated with higher expression of voltage-gated sodium channels, NaV1.7 and NaV1.8. These results highlight the importance of using multimodal profiling to better understand human sensory neuron biology and may help reveal novel therapeutic targets driving chronic pain. TeaserMapping electrical properties to transcriptional profiles of human sensory neurons offers new insights into pain neurobiology

neuroscience↗

bioRxiv2023

Bradykinin receptor expression and bradykinin-mediated sensitization of human sensory neurons

Bradykinin is a peptide implicated in inflammatory pain in both humans and rodents. In rodent sensory neurons, activation of B1 and B2 bradykinin receptors induces neuronal hyperexcitability. Recent evidence suggests that human and rodent dorsal root ganglia (DRG), which contain the cell bodies of sensory neurons, differ in the expression and function of key GPCRs and ion channels; whether BK receptor expression and function are conserved across species has not been studied in depth. In this study, we used human DRG tissue from organ donors to provide a detailed characterization of bradykinin receptor expression and bradykinin-induced changes in the excitability of human sensory neurons. We found that B2 and, to a lesser extent, B1 receptors are expressed by human DRG neurons and satellite glial cells. B2 receptors were enriched in the nociceptor subpopulation. Using patch-clamp electrophysiology, we found that acute bradykinin increases the excitability of human sensory neurons, while prolonged exposure to bradykinin decreases neuronal excitability in a subpopulation of human DRG neurons. Finally, our analyses suggest that donors history of chronic pain and age may be predictors of higher B1 receptor expression in human DRG neurons. Together, these results indicate that acute BK-induced hyperexcitability, first identified in rodents, is conserved in humans and provide further evidence supporting BK signaling as a potential therapeutic target for treating pain in humans.

neuroscience↗

bioRxiv2022

Whole genome error-corrected sequencing for sensitive circulating tumor DNA cancer monitoring

Circulating cell-free DNA (ccfDNA) sequencing for low-burden cancer monitoring is limited by sparsity of circulating tumor DNA (ctDNA), the abundance of genomic material within a plasma sample, and pre-analytical error rates due to library preparation, and sequencing errors. Sequencing costs have historically favored the development of deep targeted sequencing approaches for overcoming sparsity in ctDNA detection, but these techniques are limited by the abundance of ccfDNA in samples, which imposes a ceiling on the maximal depth of coverage in targeted panels. Whole genome sequencing (WGS) is an orthogonal approach to ctDNA detection that can overcome the low abundance of ccfDNA by supplanting sequencing depth with breadth, integrating signal across the entire tumor mutation landscape. However, the higher cost of WGS limits the practical depth of coverage and hinders broad adoption. Lower sequencing costs may thus allow for enhanced ctDNA cancer monitoring via WGS. We therefore applied emerging lower-cost WGS (Ultima Genomics, 1USD/Gb) to plasma samples at [~]120x coverage. Copy number and single nucleotide variation profiles were comparable between matched Ultima and Illumina datasets, and the deeper WGS coverage enabled ctDNA detection at the parts per million range. We further harnessed these lower sequencing costs to implement duplex error-corrected sequencing at the scale of the entire genome, demonstrating a [~]1,500x decrease in errors in the plasma of patient-derived xenograft mouse models, and error rates of [~]10-7 in patient plasma samples. We leveraged this highly de-noised plasma WGS to undertake cancer monitoring in the more challenging context of resectable melanoma without matched tumor sequencing. In this context, duplex-corrected WGS allowed us to harness known mutational signature patterns for disease monitoring without matched tumors, paving the way for de novo cancer monitoring.

genomics↗

bioRxiv2022

Machine learning guided signal enrichment for ultrasensitive plasma tumor burden monitoring

In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole genome sequencing (WGS). We now introduce MRD-EDGE, a composite machine learning-guided WGS ctDNA single nucleotide variant (SNV) and copy number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGE uses deep learning and a ctDNA-specific feature space to increase SNV signal to noise enrichment in WGS by 300X compared to our previous noise suppression platform MRDetect. MRD-EDGE also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1Gb to 200Mb, thereby expanding its applicability to a wider range of solid tumors. We harness the improved performance to track changes in tumor burden in response to neoadjuvant immunotherapy in non-small cell lung cancer and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal to noise enrichment in MRD-EDGE enables de novo mutation calling in melanoma without matched tumor, yielding clinically informative TF monitoring for patients on immune checkpoint inhibition.

genomics↗