Klarman Cell Observatory, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA.
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Bioinformatics. 2023 Jun 1;39(6). doi: 10.1093/bioinformatics/btad362.
Imaging Spatial Transcriptomics techniques characterize gene expression in cells in their native context by imaging barcoded probes for mRNA with single molecule resolution. However, the need to acquire many rounds of high-magnification imaging data limits the throughput and impact of existing methods.
We describe the Joint Sparse method for Imaging Transcriptomics, an algorithm for decoding lower magnification Imaging Spatial Transcriptomics data than that used in standard experimental workflows. Joint Sparse method for Imaging Transcriptomics incorporates codebook knowledge and sparsity assumptions into an optimization problem, which is less reliant on well separated optical signals than current pipelines. Using experimental data obtained by performing Multiplexed Error-Robust Fluorescence in situ Hybridization on tissue from mouse brain, we demonstrate that Joint Sparse method for Imaging Transcriptomics enables improved throughput and recovery performance over standard decoding methods.
Software implementation of JSIT, together with example files, is available at https://github.com/jpbryan13/JSIT.
成像空间转录组学技术通过以单分子分辨率对 mRNA 的条形码探针进行成像，在细胞的天然环境中对基因表达进行特征描述。然而，需要获取多轮高倍放大成像数据限制了现有方法的通量和影响。
我们描述了用于成像转录组学的联合稀疏方法，这是一种用于解码比标准实验工作流程中使用的更低倍放大成像空间转录组学数据的算法。用于成像转录组学的联合稀疏方法将代码本知识和稀疏性假设纳入到一个优化问题中，该问题比当前的管道更依赖于良好分离的光学信号。使用通过在来自小鼠大脑的组织上进行多路复用错误鲁棒荧光原位杂交获得的实验数据，我们证明了用于成像转录组学的联合稀疏方法能够提高标准解码方法的吞吐量和恢复性能。
JSIT 的软件实现以及示例文件可在 https://github.com/jpbryan13/JSIT 上获得。
