# Integration of hyperspectral imaging and transcriptomics from individual cells with SpectralSeq

## TL;DR

SpectralSeq combines imaging and transcriptomics to study individual cells, revealing links between optical features and gene expression in breast cancer cells.

## Contribution

SpectralSeq is a novel method that integrates hyperspectral imaging with transcriptomics for single-cell analysis.

## Key findings

- Ringed cells show higher apoptosis-related gene expression and lower ATP production gene expression.
- Larger MCF-7 cells exhibit downregulation of multiple spliceosome members.
- NADH-related fluorescence correlates with gene expression, offering insights into metabolic states.

## Abstract

Microscopy and omics are complementary approaches to probe cellular molecular states in health and disease, combining granularity with scalability. However, integrating both imaging- and sequencing-based assays on the same cell has proven challenging. This study demonstrates a new approach called SpectralSeq that combines hyperspectral autofluorescence imaging with transcriptomics on the same cell. SpectralSeq is applied to Michigan Cancer Foundation-7 (MCF-7) breast cancer cells and identifies a subpopulation of cells exhibiting bright autofluorescence rings at the plasma membrane in optical channel 13 (λex = 431 nm, λem = 594 nm). Correlating the presence of a ring with the gene expression in the same cell indicates that ringed cells show higher expression of apoptosis-related genes and lower expression of ATP production genes. Furthermore, correlation of cell morphology with gene expression reveals downregulation of multiple spliceosome members in larger MCF-7 cells. Multiple genes exhibit consistent expression across cell sizes but varied exon usage. Finally, correlation between gene expression and fluorescence within the spectral range of nicotinamide adenine dinucleotide hydrogen (NADH) provides insights into the metabolic states of MCF-7 cells. Overall, SpectralSeq links optical spectrum with internal molecular states, offering a single streamlined workflow for single-cell resolution studies integrating spectral, morphological, and transcriptomic analyses.