Jain, T. S. | bioRxiv Science

bioRxiv2026

Parvalbumin interneuron maturation reorganizes cortical population activity structure during development

During brain development, spontaneous cortical activity undergoes a dramatic transformation from highly synchronous network events to less correlated and sparse neuronal firing that is thought to underlie more flexible neural coding. However, how the structure of population activity changes across this transition, what circuit mechanisms drive this reorganization, and the implications for information encoding have not been fully established. Using longitudinal in vivo calcium imaging in early postnatal mice, we demonstrate that cortical network desynchronization during the second postnatal week is accompanied by a redistribution of fractional variance from dominant, low-dimensional modes into many weaker dimensions, and a growing influence of GABAergic interneurons (INs) on the network. All-optical circuit interrogation revealed that parvalbumin (PV) INs, but not somatostatin (SST) INs, are sufficient to desynchronize the network and shift the distribution of population variance. Through targeted ablation of a fraction of PV INs, we show dimensionality is highly correlated with stimulus decoding accuracy. Furthermore, PV INs undergo a sharp increase in transcriptional maturation between postnatal day (P) 8 and 11, during which they preferentially enrich for synaptic genes. Lastly, a novel, biologically plausible, multi-cell-type computational model reproduced the developmental desynchronization and rise in dimensionality. Strikingly, without being constrained by perturbation data, the model accurately predicted the effects of each IN class on the network that we observed empirically.

neuroscience↗

bioRxiv2020

Ethnicity-specific transcriptomic variation in immune cells and correlation with disease activity in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease in which outcomes vary among different racial groups. The aim of this study is to leverage large-scale transcriptomic data from diverse populations to better sub-classify SLE patients into more clinically actionable groups. We leverage cell sorted RNA-seq data (CD14+ monocytes, B cells, CD4+T cells, and NK cells) from 120 SLE patients (63 Asian and 57 White individuals) and apply a four tier analytical approach to identify SLE subgroups within this multiethnic cohort: unsupervised clustering, differential expression analyses, gene co-expression analyses, and machine learning. K-means clustering on the individual cell type data resulted in three clusters for CD4 and CD14, and two clusters for B cells and NK cells. Correlation analysis revealed significant positive associations between the transcriptomic clusters of each immune cell and clinical parameters including disease activity and ethnicity. We then explored differentially expressed genes between Asian and White groups for each cell-type. The shared differentially expressed genes across the four cell types were involved in SLE or other autoimmune related pathways. Co-expression analysis identified similarly regulated genes across samples and grouped these genes into modules. Samples were grouped into White-high, Asians-high (high disease activity defined by SLEDAI score >=6) and White-low, Asians-low (SLEDAI < 6). Random forest classification of disease activity in the White and Asian cohorts showed the best classification in CD4+ T cells in White. The results from these analyses will help stratify patients based on their gene expression signatures to enable precision medicine for SLE.

bioinformatics↗