henhaobio
Sign in

bioRxivGenomicsPreprint

BenchDrop-seq: a microfluidics-free platform for benchtop single-cell long-read RNA sequencing

Jamie Bregman, Calen Nichols, Rajeev Ramisetti, Avi SrivastavaiD

PDFDOI

Abstract

Single-cell long-read RNA sequencing enables direct measurement of full-length transcripts but has remained difficult to deploy at scale due to reliance on microfluidic barcoding, specialized instrumentation, and high per-cell cost. Here we present BenchDrop-seq, a benchtop platform for single-cell long-read transcriptomics that leverages particle-templated partitioning for single-cell molecular barcoding and couples this workflow to Oxford Nanopore sequencing for full-length transcript capture. By integrating established bead-based partitioning chemistry with long-read sequencing and a dedicated open-source analysis pipeline for barcode recovery, alignment, and transcript quantification, BenchDrop-seq enables isoform-resolved measurements from thousands of individual cells using standard laboratory equipment. We validate the platform in both a homogeneous cell line and a heterogeneous primary tissue, demonstrating high barcode recovery, accurate gene-level quantification, and reproducible detection of cell-type-specific transcript usage that is not readily accessible to short-read assays. Together, BenchDrop-seq establishes a practical and accessible framework for single-cell long-read RNA sequencing, lowering experimental barriers while enabling transcript-level analyses in routine single-cell experiments.

Citations
0

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISHahaut '26You '23Wang '21Trull '25Hamraoui '26Reynolds '26
73% semantic matchGenomics
Vincent Hahaut, Rebecca A. Siwicki, Mariana M. Ribeiro+4 more·bioRxiv·Mar 13, 2026