BARcode DEmixing through Non-negative Spatial Regression (BarDensr)

Abstract

Modern spatial transcriptomics methods can target thousands of different types of RNA transcripts in a single slice of tissue. Many biological applications demand a high spatial density of transcripts relative to the imaging resolution, leading to partial mixing of transcript rolonies in many pixels; unfortunately, current analysis methods do not perform robustly in this highly-mixed setting. Here we develop a new analysis approach, BARcode DEmixing through Non-negative Spatial Regression (BarDensr): we start with a generative model of the physical process that leads to the observed image data and then apply sparse convex optimization methods to estimate the underlying (demixed) rolony densities. We apply BarDensr to simulated and real data and find that it achieves state of the art signal recovery, particularly in densely-labeled regions or data with low spatial resolution. Finally, BarDensr is fast and parallelizable. We provide open-source code as well as an implementation for the 'NeuroCAAS' cloud platform.

Integrating barcoded neuroanatomy with spatial transcriptional profiling enables identification of gene correlates of projections

“…Through strong amplification of mRNAs, combinatorial coding, and robust readout using Illumina sequencing chemistry 6 , 35 , BARseq2 achieves fast imaging at low optical resolution compared to many other imaging-based spatial transcriptomic methods 14 , 36 . Further optimizations, including computational approaches for resolving spatially mixed rolonies 37 , have the potential to increase imaging throughput even further. Although the gene multiplexing capacity of BARseq2 may ultimately be limited by other physical constraints, such as crowding of rolonies and reduced detection sensitivity, these factors are unlikely to be limiting when multiplexing to dozens to hundreds of genes 11 .…”

ISTDECO: In Situ Transcriptomics Decoding by Deconvolution

“…With an 8GB laptop GPU we managed to process a 2048 × 2048 field of view with 8 rounds, 2 channels and 140 barcodes in less than 30 seconds. Even larger speedup can be obtained by adopting down-sampling strategies similar to Chen et al (2020).…”

“…We compare our proposed method with four decoders available in the Starfish library as well as with BarDensr (Chen et al , 2020).…”

“…The most closely related method — developed completely independently from ours — is BarDensr (Chen et al , 2020), which relies on a nonnegative matrix regression for unmixing and deconvolving multiplexed image data. Bar-Densr also models physical properties such as signal gain, phasing, and spectral overlap, and builds on optimising a sparsity regularised least squares problem.…”

Integrating barcoded neuroanatomy with spatial transcriptional profiling reveals cadherin correlates of projections shared across the cortex

“…Through strong amplification of mRNAs, combinatorial coding, and robust readout using Illumina sequencing chemistry (Chen et al, 2019;Chen et al, 2018), BARseq2 achieves fast imaging at low optical resolution compared to many other imaging-based spatial transcriptomic methods (Chen et al, 2015;Codeluppi et al, 2018;Eng et al, 2019;Shah et al, 2016;Zhang et al, 2020). Further optimizations, including computational approaches for resolving spatially mixed rolonies (Chen et al, 2020), have the potential to increase imaging throughput even further.…”