Biology subjects

Bartlett, D. A.

Publications and source records attributed to Bartlett, D. A..

5 recordsLinked to original sources

bioRxiv2026

Scalable single-cell isoform profiling with sequencing-by-expansion

Single-cell RNA sequencing has transformed our understanding of cellular systems, yet the reliance on short-read sequencing restricts analysis to gene-level quantification and obscures the immense biological diversity generated by alternative splicing. While long-read sequencing technologies can capture full-length RNA and resolve transcript isoforms, current platforms remain constrained by throughput and high per-base costs, rendering them impractical for modern million-cell applications. To address this critical limitation, we developed and optimized sequencing-by-expansion (SBX) chemistry for high-throughput single-cell RNA isoform profiling. Integrated within the AXELIOS 1 sequencing platform, SBX employs a unique biochemical conversion process that transforms complementary DNA into expanded surrogate high signal-to-noise polymers called Xpandomers which are sequenced via translocation through a dense nanopore array yielding over 9.5 billion reads in a two-hour run. To leverage this unique data type for long-read single-cell RNA isoform sequencing, we developed the Consensus UMI Deduplication using Longest Length (CUDLL) algorithm, which computationally consolidates variable-length raw SBX reads into single, high-fidelity consensus reads, elevating sequence accuracy to 99.83% and maximizing per transcript read length. We demonstrate that this consensus approach successfully captures the vast isoform diversity of single-cell libraries and enables the accurate measure of differential isoform expression across distinct cell types in peripheral blood mononuclear cells. Furthermore, SBX coupled with CUDLL efficiently resolves T-cell and B-cell receptor clonotypes directly from whole-transcriptome libraries without the need for VDJ-specific target enrichment. Ultimately, this work establishes SBX and the AXELIOS 1 as a transformative platform for high-scale single-cell isoform sequencing.

genetics↗

bioRxiv2026

Accurate strand-specific long-read transcript isoform discovery and quantification at bulk, single-cell, and single-nucleus resolution

Recent advances in long-read transcriptome sequencing enable high-throughput profiling of full-length RNA isoforms in bulk, single-cell, and single-nucleus samples. However, long-read datasets typically contain a mixture of complete and partial transcripts, leading to pervasive ambiguity in read-to-isoform assignment and complicating accurate isoform identification and quantification, particularly in the absence of reliable reference annotations. These challenges are further amplified in single-cell and single-nucleus samples, where coverage is sparse and transcriptional heterogeneity is high. Here, we present the Long Read Alignment Assembler (LRAA), a unified and versatile computational framework for isoform identification and quantification from long-read RNA sequencing data across bulk, single-cell, and single-nucleus transcriptomic samples. LRAA combines splice-graph based structural modeling with expectation maximization based optimization to probabilistically resolve ambiguous read assignments and improve isoform abundance estimation. The framework supports quantification-only, reference-guided, and fully reference-free (de novo) modes of analysis within a single methodological paradigm. We benchmarked LRAA using both simulated and genuine long-read datasets spanning sequencing standards and whole transcriptomes. Central to this evaluation is a novel benchmarking strategy based on Multiplexed Overexpression of Regulatory Factors (MORFs), which provides biologically expressed, barcoded isoforms with unambiguous read-level ground truth. Across all benchmarks, including MORFs, synthetic spike-ins, and whole-transcriptome datasets, LRAA consistently outperformed state-of-the-art methods in isoform identification accuracy, sensitivity, and expression quantification. Finally, we demonstrate the biological utility of LRAA by resolving cell-type-specific isoform usage across peripheral blood immune cell populations and by detecting a pathogenic cryptic isoform of STMN2 with associated transcriptional changes in single-nucleus RNA-seq data from frontal cortex tissue of an individual with frontotemporal dementia (FTD). Together, these results establish LRAA as a robust and general solution for resolving transcript diversity in complex biological systems, from development to disease.

bioinformatics↗

bioRxiv2025

Multi-platform evaluation and optimization of single-cell RNA isoform sequencing

Long-read transcriptomics enables isoform identification and quantification at single-cell resolution, but analysis is complicated by artifacts introduced during library preparation. We characterize the distinct artifact profiles of three popular single-cell platforms and demonstrate their impact on isoform identification. We introduce a new method for the identification of a wide range of artifacts in cDNA libraries and novel biochemical and bioinformatic strategies to significantly reduce their impact on downstream applications. Finally, we provide a comprehensive framework for RNA isoform sequencing analysis and interpretation as the field continues to develop.

genetics↗

bioRxiv2021

High throughput genome-wide single cell protein:DNA binding site mapping by targeted insertion of promoters (TIP-seq)

Assessing cell to cell, and importantly, chromosome to chromosome, heterogeneity in cellular phenotypes is a central goal of modern cell biology. However, chromatin profiling in single cells has been extremely challenging, and single chromosome profiling has not been achieved. In cases where single cell methods have shown promise, success has been mainly limited to histone proteins and/or require highly specialized equipment or cell type specific protocols and are relatively low throughput. Here, we have combined the advantages of tagmentation, linear amplification and combinatorial indexing to produce a high throughput single cell DNA binding site mapping method that does not require specialized equipment and is capable of multiplexing several samples/ target proteins in one experiment. Targeted Insertion of Promoters (TIP-seq) uses Tn5 fused to protein A (as with CUT&Tag) to insert a T7 RNA polymerase promoter into sites adjacent to an antibody bound to a chromatin protein of interest, followed by linear amplification of flanking DNA with T7 polymerase, cDNA preparation and PCR indexing. Tip-seq provides [~]10-fold higher unique reads and thus higher coverage per single cell compared to state-of-the-art methods. We apply TIP-seq to map histone modifications, RNA PolII and CTCF binding sites in single human and mouse cells. TIP-seq will also be adaptable for other platforms, such as 10X genomics and ICELL8. In summary, TIP-seq provides a high-throughput, low-cost method for single cell protein mapping, that yields substantially higher coverage per cell and signal to noise than existing methods.

genomics↗

bioRxiv2019

Replication timing maintains the global epigenetic state in human cells.

DNA is replicated in a defined temporal order termed the replication timing (RT) program. RT is spatially segregated in the nucleus with early/late replication corresponding to Hi-C A/B chromatin compartments, respectively. Early replication is also associated with active histone modifications and transcriptional permissiveness. However, the mechanistic interplay between RT, chromatin state, and genome compartmentalization is largely unknown. Here we report that RT is central to epigenome maintenance and compartmentalization in both human embryonic stem cells (hESCs) and cancer cell line HCT116. Knockout (KO) of the conserved RT control factor RIF1, rather than causing discrete RT switches as previously suspected, lead to dramatically increased cell to cell heterogeneity of RT genome wide, despite RIF1s enrichment in late replicating chromatin. RIF1 KO hESCs have a nearly random RT program, unlike all prior RIF1 KO cells, including HCT116, which show localized alterations. Regions that retain RT, which are prevalent in HCT116 but rare in hESCs, consist of large H3K9me3 domains revealing two independent mechanisms of RT regulation that are used to different extents in different cell types. RIF1 KO results in a striking genome wide downregulation of H3K27ac peaks and enrichment of H3K9me3 at large domains that remain late replicating, while H3K27me3 and H3K4me3 are re-distributed genome wide in a cell type specific manner. These histone modification changes coincided with global reorganization of genome compartments, transcription changes and a genome wide strengthening of TAD structures. Inducible degradation of RIF1 revealed that disruption of RT is upstream of genome compartmentalization changes. Our findings demonstrate that disruption of RT leads to widespread epigenetic mis-regulation, supporting previously speculative models in which the timing of chromatin assembly at the replication fork plays a key role in maintaining the global epigenetic state, which in turn drives genome architecture.

molecular biology↗