Biology subjects

Heaton, H.

Publications and source records attributed to Heaton, H..

11 recordsLinked to original sources

bioRxiv2026

Microchimerism in the human brain, quantitative assessment and single nuclei profiling establish cell types and diversity

Bi-directional maternal-fetal exchange during pregnancy creates a long-term microchimerism (Mc) legacy in both individuals, but its presence and cellular fate in human brain are largely unknown. We studied surgically resected epilepsy brain specimens with targetable maternal polymorphisms using polymorphism-specific quantitative PCR. Maternal Mc was prevalent, detectable in 70% of patients, and often at striking quantities spanning temporal, frontal, parietal, and hippocampal regions. Next, we employed single nucleus RNA profiling using cellector, a genetic demultiplexing tool designed to detect rare allogeneic cells. We identified Mc across major neural and glial populations. Finally, analysis of publicly available snRNA-seq datasets from neurotypical brains from gestation to late adulthood further revealed widespread Mc, persisting into advanced age, and preferentially adopting L2/3 intratelencephalic neuronal or microglial/macrophage-like fates. These data show that naturally acquired Mc is prevalent, diverse, and persistent in human brain, inviting reconsideration of what constitutes "self," with broad implications for health and disease.

neuroscience↗

bioRxiv2026

Cellector: A tool to detect foreign genotype cells in scRNAseq data with applications in leukemia and microchimerism.

The existence of rare, genetically distinct cells can occur in various samples such as transplant patients, naturally occurring microchimerism between maternal and fetal tissues, and cancer samples with sufficient mutational burden. Computational methods for detecting these foreign cells are vital to studying these biological conditions. An application that is of particular interest is that of leukemia patients post hematopoietic cell transplant (HCT). In many leukemias, a primary therapy is HCT, after which, the primary genotype of the bone marrow and blood cells should be of donor origin. If cells exist that are of the patients genotype and the cell type lineage of the particular leukemia, this is known as measurable residual disease (MRD). If the MRD is high enough, this may represent a relapse of the patients leukemia. Furthermore, accurately estimating the MRD is important for driving clinical decision making for these patients. Here we present Cellector, a computational method for identifying rare foreign genotype cells in single cell RNAseq (scRNAseq) datasets. We show cellector accurately detects microchimeric cells down to an exceedingly low percentage of these cells present (0.05% or lower).

bioinformatics↗

bioRxiv2025

Early persistence of recipient stem-cells and T-cell dysregulation are associated with relapse after transplant in AML/MDS

Hematopoietic stem cell transplantation (HSCT) offers the best curative option for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS), yet relapse remains common. Current relapse detection methods are often too late for effective intervention. To identify earlier predictors and therapeutic targets, we performed longitudinal single-cell RNA and T cell receptor (TCR) sequencing of bone marrow from 33 AML/MDS patients during post-transplant immune reconstitution, comparing those who relapsed to those who remained in remission. Persistence of recipient hematopoietic stem and progenitor cells (HSPCs) in the marrow was associated with relapse months later. These residual recipient HSPCs harbored copy number variations (CNVs), supporting their leukemic origin, and overexpressed PRAME and CALCRL compared to coexisting donor HSPCs. Further, in a subset of TP53-mutant disease, low TCR diversity with skewing toward dominant clonotypes foreshadowed relapse. These findings lay the groundwork for improved relapse prediction and nominate therapeutic targets for early post-transplant intervention.

cancer biology↗

bioRxiv2025

Souporcell3: Robust Demultiplexing for High-Donor Single-Cell RNA-seq Datasets

MotivationAccurate demultiplexing of pooled single-cell RNA-seq (scRNAseq) data is critical for large-scale studies. However, existing methods like vireo, while effective up to [~]16 donors, often struggle with poor clustering due to local optima as donor numbers rise. In high-donor scenarios, overlapping genotypes, a dense genotype space, and increased doublet formation make demultiplexing challenging, requiring methods that are robust to sparse, high-dimensional data and maintain reliable accuracy even as sample complexity grows. ResultsWe present an enhanced version of souporcell capable of demultiplexing up to 64 donors. The method uses 10x merge for initialization, K-Harmonic Means for robust clustering, and iterative refinement with reinitialization of low-quality clusters and locking of high-quality ones. Compared to vireo, vireo with overclustering, and the original souporcell, our approach completely eliminates duplicate clusters and achieves consistently high Adjusted Rand Index (ARI) scores across various doublet rates, demonstrating improved accuracy and scalability. AvailabilitySouporcell3 source code and documentation are released on GitHub: https://github.com/wheaton5/souporcell

bioinformatics↗

bioRxiv2024

LCSKPOA: Enabling banded semi-global partial order alignments via efficient and accurate backbone generation through extended lcsk++

BackgroundMost multiple sequence alignment and string-graph alignment algorithms focus on global alignment, but many applications exist for semi-global and local string-graph alignment. Long reads require enormous amounts of memory and runtime to fill out large dynamic programming tables. Effective algorithms for finding the backbone and thus defining a band of an alignment such as the longest common subsequence with kmer matches (LCSk++) exist but do not work with graphs. This study introduces an adaptation of the Longest Common Subsequence with kmer matches (LCSk++) algorithm tailored for graph structures, particularly focusing on Partial Order Alignment (POA) graphs. POA graphs, which are directed acyclic graphs, represent multiple sequence alignments and effectively capture the relationships between sequences. Current state of the art methods like ABPOA and SPOA, while improving POA, primarily focus on global alignment and thus are limited in local and semi-global banding scenarios. Our approach addresses these limitations by extending the LCSk++ algorithm to accommodate the complexities of graph-based alignment. ResultsOur extended LCSk++ algorithm integrates dynamic programming and graph traversal techniques to detect conserved regions within POA graphs, termed the LCSk++ backbone. This backbone enables precise banding of the POA matrix for local and semi-global alignment, significantly enhancing the construction of consensus sequences. Compared to unbanded semi-global POA, our method demonstrates substantial memory savings (up to 98%) and significant run-time reductions (up to 37-fold), particularly for long sequences. The method maintains high alignment scores and proves effective across various string lengths and datasets, including synthetic and PacBio HiFi reads. Parallel processing further enhances runtime efficiency, achieving up to 150x speed improvements on conventional PCs. ConclusionThe extended LCSk++ algorithm for graph structures offers a substantial advancement in sequence alignment technology. It effectively reduces memory consumption and optimizes run times without compromising alignment quality, thus providing a robust solution for local and semi-global alignment in POA graphs. This method enhances the utility of POA in critical applications such as multiple sequence alignment for phylogeny construction and graph-based reference alignment.

genomics↗

bioRxiv2024

TopoQual polishes circular consensus sequencing data and accurately predicts quality scores

SummaryPacific Biosciences (PacBio) circular consensus sequencing (CCS) aka high fidelity (HiFi) technology has revolutionized modern genomics by producing long (10+kb) and highly accurate reads by sequencing circularized DNA molecules multiple times and combining them into a consensus sequence. Currently the accuracy and quality value estimation is more than sufficient for genome assembly and germline variant calling, but the estimated quality scores are not accurate enough for confident somatic variant calling on single reads. Here we introduce TopoQual, a tool utilizing partial order alignments (POA), topologically parallel bases, and deep learning to polish consensus sequences and more accurately predict base qualities. We correct ~31.9% of errors in PacBio consensus sequences and validate base qualities up to q59 which is one error in 0.9 million bases enabling accurate somatic variant calling with HiFi data. Availability and implementationThe source code and installation instructions as well as validation dataset used are freely available at https://github.com/lorewar2/TopoQual

bioinformatics↗

bioRxiv2019

souporcell: Robust clustering of single cell RNAseq by genotype and ambient RNA inference without reference genotypes

Methods to deconvolve single-cell RNA sequencing (scRNAseq) data are necessary for samples containing a natural mixture of genotypes and for scRNAseq experiments that multiplex cells from different donors1. Multiplexing across donors is a popular experimental design with many benefits including avoiding batch effects2, reducing costs, and improving doublet detection. Using variants detected in the RNAseq reads, it is possible to assign cells to the individuals from which they arose. These variants can also be used to identify and remove cross-genotype doublet cells that may have highly similar transcriptional profiles precluding detection by transcriptional profile. More subtle cross-genotype variant contamination can be used to estimate the amount of ambient RNA in the system. Ambient RNA is caused by cell lysis prior to droplet partitioning and is an important confounder of scRNAseq analysis3. Souporcell is a novel method to cluster cells using only the genetic variants detected within the scRNAseq reads. We show that it achieves high accuracy on genotype clustering, doublet detection, and ambient RNA estimation as demonstrated across a wide range of challenging scenarios.

bioinformatics↗

bioRxiv2019

The Malaria Cell Atlas: a comprehensive reference of single parasite transcriptomes across the complete Plasmodium life cycle

Malaria parasites adopt a remarkable variety of morphological life stages as they transition through multiple mammalian host and mosquito vector environments. Here we profile the single-cell transcriptomes of thousands of individual parasites, deriving the first high-resolution transcriptional atlas of the entire Plasmodium berghei life cycle. We then use our atlas to precisely define developmental stages of single cells from three different human malaria parasite species, including parasites isolated directly from infected individuals. The Malaria Cell Atlas provides both a comprehensive view of gene usage in a complex eukaryotic parasite and an open access reference data set for the study of malaria parasites.

genomics↗

bioRxiv2018

A High-Quality De Novo Genome Assembly from a Single Mosquito using PacBio Sequencing

A high-quality reference genome is a fundamental resource for functional genetics, comparative genomics, and population genomics, and is increasingly important for conservation biology. PacBio Single Molecule, Real-Time (SMRT) sequencing generates long reads with uniform coverage and high consensus accuracy, making it a powerful technology for de novo genome assembly. Improvements in throughput and concomitant reductions in cost have made PacBio an attractive core technology for many large genome initiatives, however, relatively high DNA input requirements ([~]5 {micro}g for standard library protocol) have placed PacBio out of reach for many projects on small organisms that have lower DNA content, or on projects with limited input DNA for other reasons. Here we present a high-quality de novo genome assembly from a single Anopheles coluzzii mosquito. A modified SMRTbell library construction protocol without DNA shearing and size selection was used to generate a SMRTbell library from just 100 ng of starting genomic DNA. The sample was run on the Sequel System with chemistry 3.0 and software v6.0, generating, on average, 25 Gb of sequence per SMRT Cell with 20 hour movies, followed by diploid de novo genome assembly with FALCON-Unzip. The resulting curated assembly had high contiguity (contig N50 3.5 Mb) and completeness (more than 98% of conserved genes are present and full-length). In addition, this single-insect assembly now places 667 (>90%) of formerly unplaced genes into their appropriate chromosomal contexts in the AgamP4 PEST reference. We were also able to resolve maternal and paternal haplotypes for over 1/3 of the genome. By sequencing and assembling material from a single diploid individual, only two haplotypes are present, simplifying the assembly process compared to samples from multiple pooled individuals. The method presented here can be applied to samples with starting DNA amounts as low as 100 ng per 1 Gb genome size. This new low-input approach puts PacBio-based assemblies in reach for small highly heterozygous organisms that comprise much of the diversity of life.

genomics↗

bioRxiv2018

Reproducible integration of multiple sequencing datasets to form high-confidence SNP, indel, and reference calls for five human genome reference materials

Benchmark small variant calls from the Genome in a Bottle Consortium (GIAB) for the CEPH/HapMap genome NA12878 (HG001) have been used extensively for developing, optimizing, and demonstrating performance of sequencing and bioinformatics methods. Here, we develop a reproducible, cloud-based pipeline to integrate multiple sequencing datasets and form benchmark calls, enabling application to arbitrary human genomes. We use these reproducible methods to form high-confidence calls with respect to GRCh37 and GRCh38 for HG001 and 4 additional broadly-consented genomes from the Personal Genome Project that are available as NIST Reference Materials. These new genomes broad, open consent with few restrictions on availability of samples and data is enabling a uniquely diverse array of applications. Our new methods produce 17% more high-confidence SNPs, 176% more indels, and 12% larger regions than our previously published calls. To demonstrate that these calls can be used for accurate benchmarking, we compare other high-quality callsets to ours (e.g., Illumina Platinum Genomes), and we demonstrate that the majority of discordant calls are errors in the other callsets, We also highlight challenges in interpreting performance metrics when benchmarking against imperfect high-confidence calls. We show that benchmarking tools from the Global Alliance for Genomics and Health can be used with our calls to stratify performance metrics by variant type and genome context and elucidate strengths and weaknesses of a method.

genomics↗

bioRxiv2017

Resolving the Full Spectrum of Human Genome Variation using Linked-Reads

Large-scale population based analyses coupled with advances in technology have demonstrated that the human genome is more diverse than originally thought. To date, this diversity has largely been uncovered using short read whole genome sequencing. However, standard short-read approaches, used primarily due to accuracy, throughput and costs, fail to give a complete picture of a genome. They struggle to identify large, balanced structural events, cannot access repetitive regions of the genome and fail to resolve the human genome into its two haplotypes. Here we describe an approach that retains long range information while harnessing the advantages of short reads. Starting from only [~]1ng of DNA, we produce barcoded short read libraries. The use of novel informatic approaches allows for the barcoded short reads to be associated with the long molecules of origin producing a novel datatype known as Linked-Reads. This approach allows for simultaneous detection of small and large variants from a single Linked-Read library. We have previously demonstrated the utility of whole genome Linked-Reads (lrWGS) for performing diploid, de novo assembly of individual genomes (Weisenfeld et al. 2017). In this manuscript, we show the advantages of Linked-Reads over standard short read approaches for reference based analysis. We demonstrate the ability of Linked-Reads to reconstruct megabase scale haplotypes and to recover parts of the genome that are typically inaccessible to short reads, including phenotypically important genes such as STRC, SMN1 and SMN2. We demonstrate the ability of both lrWGS and Linked-Read Whole Exome Sequencing (lrWES) to identify complex structural variations, including balanced events, single exon deletions, and single exon duplications. The data presented here show that Linked-Reads provide a scalable approach for comprehensive genome analysis that is not possible using short reads alone.

genomics↗