A Curated Compendium of Transcriptomic Data for the Exploration of Neocortical Development.

Vast quantities of multi-omic data have been produced to characterize the development and diversity of cell types in the cerebral cortex of humans and other mammals. To more fully harness the collective discovery potential of these data, we have assembled gene-level transcriptomic data from 188 published studies of neocortical development, including the transcriptomes of ~30 million single-cells, extensive spatial transcriptomic experiments and RNA sequencing of sorted cells and bulk tissues: nemoanalytics.org/landing/neocortex. Applying joint matrix decomposition (SJD) to mouse, macaque and human data in this collection, we defined transcriptome dynamics that are conserved across mammalian neurogenesis and which elucidate the evolution of outer, or basal, radial glial cells. Decomposition of adult human neocortical data identified layer-specific signatures in mature neurons and, in combination with transfer learning methods in NeMO Analytics, enabled the charting of their early developmental emergence and protracted maturation across years of postnatal life. Interrogation of data from cerebral organoids demonstrated that while broad molecular elements of in vivo development are recapitulated in vitro, many layer-specific transcriptomic programs in neuronal maturation are absent. We invite computational biologists and cell biologists without coding expertise to use NeMO Analytics in their research and to fuel it with emerging data (carlocolantuoni.org).

Co-expression of calcium and hERG potassium channels reduces the incidence of proarrhythmic events.

AIMS: Cardiac electrical activity is extraordinarily robust. However, when it goes wrong it can have fatal consequences. Electrical activity in the heart is controlled by the carefully orchestrated activity of more than a dozen different ion conductances. While there is considerable variability in cardiac ion channel expression levels between individuals, studies in rodents have indicated that there are modules of ion channels whose expression co-vary. The aim of this study was to investigate whether meta-analytic co-expression analysis of large-scale gene expression datasets could identify modules of co-expressed cardiac ion channel genes in human hearts that are of functional importance. METHODS AND RESULTS: Meta-analysis of 3653 public human RNA-seq datasets identified a strong correlation between expression of CACNA1C (L-type calcium current, ICaL) and KCNH2 (rapid delayed rectifier K+ current, IKr), which was also observed in human adult heart tissue samples. In silico modelling suggested that co-expression of CACNA1C and KCNH2 would limit the variability in action potential duration seen with variations in expression of ion channel genes and reduce susceptibility to early afterdepolarizations, a surrogate marker for proarrhythmia. We also found that levels of KCNH2 and CACNA1C expression are correlated in human-induced pluripotent stem cell-derived cardiac myocytes and the levels of CACNA1C and KCNH2 expression were inversely correlated with the magnitude of changes in repolarization duration following inhibition of IKr. CONCLUSION: Meta-analytic approaches of multiple independent human gene expression datasets can be used to identify gene modules that are important for regulating heart function. Specifically, we have verified that there is co-expression of CACNA1C and KCNH2 ion channel genes in human heart tissue, and in silico analyses suggest that CACNA1C-KCNH2 co-expression increases the robustness of cardiac electrical activity.

Spatial coherence and stationarity of local field potentials in an isolated whole hippocampal preparation in vitro.

Local field potential (LFP) multielectrode recordings of spontaneous rhythms in an isolated whole hippocampal preparation are characterized with respect to their spatial variability within the hippocampus, and their frequency properties. Using simulated data, we categorize potential relationships between frequency variation over time in LFP recordings and spatial variability between electrodes. We then use data recorded from the intact preparation to distinguish between our theoretical categories. We find that the LFP recordings have a close to spatially invariant frequency distribution (not phase) across the hippocampus, and differ in frequency only in a component that may be seen as physiological noise. From these facts, we conclude that the isolated hippocampal LFP recordings represent a single signal and may be regarded as a unitary circuitry. We additionally examine phase differences across our recording sites. We use our characterization of the hippocampal isolate's properties to predict its spatial coherence in response to high frequency stimulation. We find that there is a finely tuned inverse relationship between temporal variability in the hippocampal isolate's LFP recordings and their spatial coherence.

Is it time to change the reference genome?

The use of the human reference genome has shaped methods and data across modern genomics. This has offered many benefits while creating a few constraints. In the following opinion, we outline the history, properties, and pitfalls of the current human reference genome. In a few illustrative analyses, we focus on its use for variant-calling, highlighting its nearness to a 'type specimen'. We suggest that switching to a consensus reference would offer important advantages over the continued use of the current reference with few disadvantages.