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Although corticothalamic neurons (CThNs) represent the largest source of synaptic input to thalamic neurons, their role in regulating thalamocortical interactions remains incompletely understood. CThNs in sensory cortex have historically been divided into two types, those with cell bodies in Layer 6 (L6) that project back to primary sensory thalamic nuclei and those with cell bodies in Layer 5 (L5) that project to higher‐order thalamic nuclei and subcortical structures. Recently, diversity among L6 CThNs has increasingly been appreciated. In the rodent somatosensory cortex, two major classes of L6 CThNs have been identified: one projecting to the ventral posterior medial nucleus (VPM‐only L6 CThNs) and one projecting to both VPM and the posterior medial nucleus (VPM/POm L6 CThNs). Using rabies‐based tracing methods in mice, we asked whether these L6 CThN populations integrate similar synaptic inputs. We found that both types of L6 CThNs received local input from somatosensory cortex and thalamic input from VPM and POm. However, VPM/POm L6 CThNs received significantly more input from a number of additional cortical areas, higher order thalamic nuclei, and subcortical structures. We also found that the two types of L6 CThNs target different functional regions within the thalamic reticular nucleus (TRN). Together, our results indicate that these two types of L6 CThNs represent distinct information streams in the somatosensory cortex and suggest that VPM‐only L6 CThNs regulate, via their more restricted circuits, sensory responses related to a cortical column while VPM/POm L6 CThNs, which are integrated into more widespread POm‐related circuits, relay contextual information.

 

Abstract Background The cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data. Results Here, we present tricycle, an R/Bioconductor package, to address this challenge by leveraging key features of the biology of the cell cycle, the mathematical properties of principal component analysis of periodic functions, and the use of transfer learning. We estimate a cell-cycle embedding using a fixed reference dataset and project new data into this reference embedding, an approach that overcomes key limitations of learning a dataset-dependent embedding. Tricycle then predicts a cell-specific position in the cell cycle based on the data projection. The accuracy of tricycle compares favorably to gold-standard experimental assays, which generally require specialized measurements in specifically constructed in vitro systems. Using internal controls which are available for any dataset, we show that tricycle predictions generalize to datasets with multiple cell types, across tissues, species, and even sequencing assays. Conclusions Tricycle generalizes across datasets and is highly scalable and applicable to atlas-level single-cell RNA-seq data. 

The cerebral cortex contains numerous neuronal cell types, distinguished by their molecular identity as well as their electrophysiological and morphological properties. Cortical function is reliant on stereotyped patterns of synaptic connectivity and synaptic function among these neuron types, but how these patterns are established during development remains poorly understood. Selective targeting not only of different cell types but also of distinct postsynaptic neuronal domains occurs in many brain circuits and is directed by multiple mechanisms. These mechanisms include the regulation of axonal and dendritic guidance and fine-scale morphogenesis of pre- and postsynaptic processes, lineage relationships, activity dependent mechanisms and intercellular molecular determinants such as transmembrane and secreted molecules, many of which have also been implicated in neurodevelopmental disorders. However, many studies of synaptic targeting have focused on circuits in which neuronal processes target different lamina, such that cell-type-biased connectivity may be confounded with mechanisms of laminar specificity. In the cerebral cortex, each cortical layer contains cell bodies and processes from intermingled neuronal cell types, an arrangement that presents a challenge for the development of target-selective synapse formation. Here, we address progress and future directions in the study of cell-type-biased synaptic targeting in the cerebral cortex. We highlight challenges to identifying developmental mechanisms generating stereotyped patterns of intracortical connectivity, recent developments in uncovering the determinants of synaptic target selection during cortical synapse formation, and current gaps in the understanding of cortical synapse specificity. 

Abstract Motivation Dimension reduction techniques are widely used to interpret high-dimensional biological data. Features learned from these methods are used to discover both technical artifacts and novel biological phenomena. Such feature discovery is critically importent in analysis of large single-cell datasets, where lack of a ground truth limits validation and interpretation. Transfer learning (TL) can be used to relate the features learned from one source dataset to a new target dataset to perform biologically driven validation by evaluating their use in or association with additional sample annotations in that independent target dataset. Results We developed an R/Bioconductor package, projectR, to perform TL for analyses of genomics data via TL of clustering, correlation and factorization methods. We then demonstrate the utility TL for integrated data analysis with an example for spatial single-cell analysis. Availability and implementation projectR is available on Bioconductor and at https://github.com/genesofeve/projectR. Contact gsteinobrien@jhmi.edu or ejfertig@jhmi.edu Supplementary information Supplementary data are available at Bioinformatics online.