Recent progress in fluorescent protein development has generated a large diversity of near-infrared fluorescent proteins (NIR FPs), which are rapidly becoming popular probes for a variety of imaging applications. However, the diversity of NIR FPs poses a challenge for end-users in choosing the optimal one for a given application. Here we conducted a systematic and quantitative assessment of intracellular brightness, photostability, oligomeric state, chemical stability and cytotoxicity of 22 NIR FPs in cultured mammalian cells and primary mouse neurons and identified a set of top-performing FPs including emiRFP670, miRFP680, miRFP713 and miRFP720, which can cover a majority of imaging applications. The top-performing proteins were further validated for in vivo imaging of neurons in Caenorhabditis elegans, zebrafish, and mice as well as in mice liver. We also assessed the applicability of the selected NIR FPs for multicolor imaging of fusions, expansion microscopy and two-photon imaging.

Single-cell RNA-sequencing (scRNA-seq) is an indispensable tool for characterizing cellular diversity and generating hypotheses throughout biology. Droplet-based scRNA-seq datasets often lack expression data for genes that can be detected with other methods. Here we show that the observed sensitivity deficits stem from three sources: (1) poor annotation of 3' gene ends; (2) issues with intronic read incorporation; and (3) gene overlap-derived read loss. We show that missing gene expression data can be recovered by optimizing the reference transcriptome for scRNA-seq through recovering false intergenic reads, implementing a hybrid pre-mRNA mapping strategy and resolving gene overlaps. We demonstrate, with a diverse collection of mouse and human tissue data, that reference optimization can substantially improve cellular profiling resolution and reveal missing cell types and marker genes. Our findings argue that transcriptomic references need to be optimized for scRNA-seq analysis and warrant a reanalysis of previously published datasets and cell atlases.