BICCN

Scaling up spatial RNA profiling with compressed sensing

 

Single cell RNA-Seq (scRNA-Seq) and Imaging Transcriptomics (IT) methods have put a systematic understanding of the brain through comprehensive 3-dimensional maps of its constituent cell types within reach. However, scRNA-Seq lacks spatial information, whereas Imaging Transcriptomics methods do not yet collect at transcriptome scale and are hampered by low throughput. In IT methods, samples are passed through multiple rounds of multi-color imaging with single-molecule resolution, and the sequence of colors originating from individual molecules is used to assign each molecule to a gene identity, encoded by a molecularly designed codebook. Currently, analyzing an entire mouse brain with IT would require years of instrument time, while a human brain would require thousands of times longer. This project aims to dramatically scale up the throughput of IT imaging in genes, time, and space, by approaching this problem through the mathematics of compressed sensing. The number of samples—and acquisition time—necessary to recover the underlying data at transcriptome scale will be decreased by developing suitable models of the underlying information. Information will be compressed along three orthogonal axes:

1. The number of genes profiled will be increased by exploiting previous results indicating that full transcriptomic information can be extracted from the activities of a small number of composite measurements of multiple genes simultaneously. An experimental method to measure these gene composites in situ will be developed, followed by a computational decompression to recover the spatial transcriptomic profile of each gene.
2. The number of rounds of imaging necessary to assign gene identity to an RNA molecule will be decreased by a combined optical and compressed sensing approach to increase the number of color channels recorded simultaneously while decreasing the total imaging and on-stage chemistry time.
3. The number of pixels which must be sampled will be decreased, allowing imaging at lower magnification.

When combined, these three independent aims should result in a ~25,000-fold increase in throughput compared to existing state-of-the-art IT measurements.

The combined power of these methods will be demonstrated by generating a spatially resolved, full transcriptome-depth atlas of the mouse primary motor and somatosensory cortices. These approaches will apply to multiple IT methods and will accelerate transcriptomic tissue mapping efforts in health and disease.

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