Pinpointing Cell Identity in Time and Space

Abstract

Mammalian cells display a broad spectrum of phenotypes, morphologies, and functional niches within biological systems. Our understanding of mechanisms at the individual cellular level, and how cells function in concert to form tissues, organs and systems, has been greatly facilitated by centuries of extensive work to classify and characterize cell types. Classic histological approaches are now complemented with advanced single-cell sequencing and spatial transcriptomics for cell identity studies. Emerging data suggests that additional levels of information should be considered, including the subcellular spatial distribution of molecules such as RNA and protein, when classifying cells. In this Perspective piece we describe the importance of integrating cell transcriptional state with tissue and subcellular spatial and temporal information for thorough characterization of cell type and state. We refer to recent studies making use of single cell RNA-seq and/or image-based cell characterization, which highlight a need for such in-depth characterization of cell populations. We also describe the advances required in experimental, imaging and analytical methods to address these questions. This Perspective concludes by framing this argument in the context of projects such as the Human Cell Atlas, and related fields of cancer research and developmental biology.

LISA2: Learning Complex Single-Cell Trajectory and Expression Trends

“…But cell states are dynamic. Branch points in the trajectory may be hypothetical and lagged behind the real cell fate decision ( Tritschler et al, 2019 ; Lähnemann et al, 2020 ; Savulescu et al, 2020 ; Wagner and Klein, 2020 ). These factors may disturb feature selection, cell clustering, and visualization.…”

Analyzing Modern Biomolecules: The Revolution of Nucleic-Acid Sequencing – Review

“…On the other hand, cell identity is determined in different ways, with transcription factor (TF) networks playing an essential role. Recent developments in nucleic-acid sequencing, in general, and sc/snRNA-seq, in particular, allow to couple transcriptomic maps with cell identity, defining profiles of gene expression for each cell [110][111][112][113][114][115]. Interestingly, although pseudogenes were considered functionless, TGS has allowed to identify many transcribed pseudogenes, including protein-coding ones in normal and cancer human cells [116].…”

Prediction of RNA subcellular localization: Learning from heterogeneous data sources

“…Using solely single-cell genomics approaches, which are standard in cell state characterization studies, spatial information of RNA transcripts might be lost. For example, two neighboring cells in a tissue, which possess similar concentrations of the same RNA transcripts, would be classified by single-cell genomics approaches as the same cell type/state; however, the RNAs in these cells may exhibit marked patterns in subcellular dispersion ( Savulescu et al., 2020 ) ( Figure 1 , Panel B). This again emphasizes the significance of characterizing subcellular distribution alongside expression levels of RNA transcripts for a more thorough classification of cell subtype or/and state.…”