Single-cell RNA sequencing of batch Chlamydomonas cultures reveals heterogeneity in their diurnal cycle phase.
Feiyang MaPatrice A SaloméSabeeha S MerchantMatteo PellegriniPublished in: The Plant cell (2021)
The photosynthetic unicellular alga Chlamydomonas (Chlamydomonas reinhardtii) is a versatile reference for algal biology because of its ease of culture in the laboratory. Genomic and systems biology approaches have previously described transcriptome responses to environmental changes using bulk data, thus representing the average behavior from pools of cells. Here, we apply single-cell RNA sequencing (scRNA-seq) to probe the heterogeneity of Chlamydomonas cell populations under three environments and in two genotypes differing by the presence of a cell wall. First, we determined that RNA can be extracted from single algal cells with or without a cell wall, offering the possibility to sample natural algal communities. Second, scRNA-seq successfully separated single cells into nonoverlapping cell clusters according to their growth conditions. Cells exposed to iron or nitrogen deficiency were easily distinguished despite a shared tendency to arrest photosynthesis and cell division to economize resources. Notably, these groups of cells not only recapitulated known patterns observed with bulk RNA-seq but also revealed their inherent heterogeneity. A substantial source of variation between cells originated from their endogenous diurnal phase, although cultures were grown in constant light. We exploited this result to show that circadian iron responses may be conserved from algae to land plants. We document experimentally that bulk RNA-seq data represent an average of typically hidden heterogeneity in the population.
Keyphrases
- single cell
- rna seq
- induced apoptosis
- high throughput
- cell cycle arrest
- gene expression
- cell death
- signaling pathway
- endoplasmic reticulum stress
- mesenchymal stem cells
- stem cells
- dna methylation
- electronic health record
- risk assessment
- replacement therapy
- big data
- single molecule
- cell therapy
- deep learning
- cell cycle
- bone marrow
- cell proliferation
- genetic diversity