The recent technological and computational advances in mass spectrometry-based single-cell proteomics have pushed the boundaries of sensitivity and throughput. However, reproducible quantification of thousands of proteins within a single cell remains challenging. To address some of those limitations, we present a dedicated sample preparation chip, the proteoCHIP EVO 96 that directly interfaces with the Evosep One. This, in combination with the Bruker timsTOF demonstrates double the identifications without manual sample handling and the newest generation timsTOF Ultra identifies up to 4000 with an average of 3500 protein groups per single HEK-293T without a carrier or match-between runs. Our workflow spans 4 orders of magnitude, identifies over 50 E3 ubiquitin-protein ligases, and profiles key regulatory proteins upon small molecule stimulation. This study demonstrates that the proteoCHIP EVO 96-based sample preparation with the timsTOF Ultra provides sufficient proteome depth to study complex biology beyond cell-type classifications.

How plants determine the final size of growing cells is an important, yet unresolved, issue. Root hairs provide an excellent model system with which to study this as their final cell size is remarkably constant under constant environmental conditions. Previous studies have demonstrated that a basic helix-loop helix transcription factor ROOT HAIR DEFECTIVE 6-LIKE 4 (RSL4) promotes root hair growth, but how hair growth is terminated is not known. In this study, we demonstrate that a trihelix transcription factor GT-2-LIKE1 (GTL1) and its homolog DF1 repress root hair growth in Arabidopsis. Our transcriptional data, combined with genome-wide chromatin-binding data, show that GTL1 and DF1 directly bind the RSL4 promoter and regulate its expression to repress root hair growth. Our data further show that GTL1 and RSL4 regulate each other, as well as a set of common downstream genes, many of which have previously been implicated in root hair growth. This study therefore uncovers a core regulatory module that fine-tunes the extent of root hair growth by the orchestrated actions of opposing transcription factors.

Significance: We developed a computational pipeline that uses gene expression datasets for inferring relationships among genes and predicting their importance. We showed that the capacity of our pipeline to integrate spatial and temporal transcriptional datasets improves the performance of inference algorithms. The combination of this pipeline withArabidopsisstem cell-specific data resulted in networks that capture the regulations of stem cell-enriched genes in the stem cells and throughout root development. Our combined approach of molecular biology, computational biology, and mathematical biology, led to successful findings of factors that could play important roles in stem cell regulation and, in particular, quiescent center function.

Brassinosteroids (BRs) are plant steroid hormones that regulate cell division and stress response. Here we use a systems biology approach to integrate multi-omic datasets and unravel the molecular signaling events of BR response in Arabidopsis. We profile the levels of 26,669 transcripts, 9,533 protein groups, and 26,617 phosphorylation sites from Arabidopsis seedlings treated with brassinolide (BL) for six different lengths of time. We then construct a network inference pipeline called Spatiotemporal Clustering and Inference of Omics Networks (SC-ION) to integrate these data. We use our network predictions to identify putative phosphorylation sites on BES1 and experimentally validate their importance. Additionally, we identify BRONTOSAURUS (BRON) as a transcription factor that regulates cell division, and we show that BRON expression is modulated by BR-responsive kinases and transcription factors. This work demonstrates the power of integrative network analysis applied to multi-omic data and provides fundamental insights into the molecular signaling events occurring during BR response.