Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input–output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1–5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.

Comparing the impact of the COVID-19 pandemic between countries or across time is difficult because the reported numbers of cases and deaths can be strongly affected by testing capacity and reporting policy. Excess mortality, defined as the increase in all-cause mortality relative to the recent average, is widely considered as a more objective indicator of the COVID-19 death toll. However, there has been no central, frequently-updated repository of the all-cause mortality data across countries. To fill this gap, we have collected weekly, monthly, or quarterly all-cause mortality data from 77 countries, openly available as the regularly-updated World Mortality Dataset. We used this dataset to compute the excess mortality in each country during the COVID-19 pandemic. We found that in the worst-affected countries the annual mortality increased by over 50%, while in several other countries it decreased by over 5%, presumably due to lockdown measures decreasing the non-COVID mortality. Moreover, we found that while some countries have been reporting the COVID-19 deaths very accurately, many countries have been underreporting their COVID-19 deaths by an order of magnitude or more. Averaging across the entire dataset suggests that the world’s COVID-19 death toll may be at least 1.6 times higher than the reported number of confirmed deaths.

Single-cell transcriptomics yields ever growing data sets containing RNA expression levels for thousands of genes from up to millions of cells. Common data analysis pipelines include a dimensionality reduction step for visualising the data in two dimensions, most frequently performed using t-distributed stochastic neighbour embedding (t-SNE). It excels at revealing local structure in high-dimensional data, but naive applications often suffer from severe shortcomings, e.g. the global structure of the data is not represented accurately. Here we describe how to circumvent such pitfalls, and develop a protocol for creating more faithful t-SNE visualisations. It includes PCA initialisation, a high learning rate, and multi-scale similarity kernels; for very large data sets, we additionally use exaggeration and downsampling-based initialisation. We use published single-cell RNA-seq data sets to demonstrate that this protocol yields superior results compared to the naive application of t-SNE.

One of the most ubiquitous analysis tools employed in single-cell transcriptomics and cytometry is t-distributed stochastic neighbor embedding (t-SNE) [1], used to visualize individual cells as points on a 2D scatter plot such that similar cells are positioned close together. Recently, a related algorithm, called uniform manifold approximation and projection (UMAP) [2] has attracted substantial attention in the single-cell community. In Nature Biotechnology, Becht et al. [3] argued that UMAP is preferable to t-SNE because it better preserves the global structure of the data and is more consistent across runs. Here we show that this alleged superiority of UMAP can be entirely attributed to different choices of initialization in the implementations used by Becht et al.: t-SNE implementations by default used random initialization, while the UMAP implementation used a technique called Laplacian eigenmaps [4] to initialize the embedding. We show that UMAP with random initialization preserves global structure as poorly as t-SNE with random initialization, while t-SNE with informative initialization performs as well as UMAP with informative initialization. Hence, contrary to the claims of Becht et al., their experiments do not demonstrate any advantage of the UMAP algorithm per se, but rather warn against using random initialization.

22Layer 4 (L4) of mammalian neocortex plays a crucial role in cortical information processing, yet 23 a complete census of its cell types and connectivity remains elusive. Using whole-cell 24 recordings with morphological recovery, we identified one major excitatory and seven inhibitory 25 types of neurons in L4 of adult mouse visual cortex (V1). Nearly all excitatory neurons were 26 pyramidal and almost all Somatostatin-positive (SOM + ) neurons were Martinotti cells. In 27 contrast, in somatosensory cortex (S1), excitatory cells were mostly stellate and SOM + cells 28 were non-Martinotti. These morphologically distinct SOM + interneurons correspond to different 29 transcriptomic cell types and are differentially integrated into the local circuit with only S1 cells 30 receiving local excitatory input. Our results challenge the classical view of a canonical 31 microcircuit repeated through the neocortex. Instead we propose that cell-type specific circuit 32 motifs, such as the Martinotti/pyramidal pair, are optionally used across the cortex as building 33 blocks to assemble cortical circuits. 34 3 Main 35The mammalian sensory neocortex is organized in a six-layered structure. In this stereotypical 36 architecture, layer 4 (L4) is the main target of sensory inputs coming from the thalamus, thus 37 acting as the first level of cortical processing for sensory signals. Understanding how L4 38 implements its computations requires to characterize the diversity of its constituent neuronal 39 components and the connectivity among them. 40 41Most previous studies of L4 have focused on primary somatosensory cortex (S1) of young rats 42 and mice. Spiny stellate cells have been reported to be the dominant excitatory neural type, 43 both in rat 1,2 and in mouse after postnatal day five (as a result of sculpting of initially pyramidal 44 neurons during development) 3 . In contrast, inhibitory interneurons are highly diverse in terms of 45 their genetic markers, morphologies and electrophysiological properties. Previous studies have 46 reported three types of fast-spiking (FS), parvalbumin-positive (PV + ) interneurons 4 and five 47 types of non-FS interneurons 5 , all of which have distinct morphologies. Several recent studies 48 revealed that the somatostatin-positive (SOM + ) interneurons form a single morphological 49 population that has been called non-Martinotti cells 6 since their axons mainly target L4 7,8 , in 50 contrast to typical Martinotti cells, which target L1. Interneuron types exhibit type-specific 51 connectivity patterns. For example, PV + FS interneurons receive thalamic inputs 9-13 but SOM + 52 non-FS interneurons do not 14 . Both groups are reciprocally connected to local excitatory 53 neurons and between each other 4,8,10,12,15,16 , but PV + inhibit each other while SOM + do not 17 . 54 55Since most of these detailed studies were performed in S1 of young animals, it is unclear 56 whether the cellular components of L4 and their connectivity profile are the same in other 57 cortical areas and in adult anima...