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1. Three-dimensional intact-tissue sequencing of single-cell transcriptional states.

   Wang, X. (June 2018, Science)

   The mammalian brain consists of an intricate tapestry of cell types, with diversity crucial for function that arises from both differential gene expression and circuit-specific anatomy. Yet, retrieving high-content gene-expression information while retaining 3D positional anatomy at cellular resolution has been difficult, limiting integrative understanding of brain structure and function. Here we introduce and apply a technology for 3D intact-tissue RNA sequencing, termed STARmap (Spatially-resolved Transcript Amplicon Readout Mapping), which integrates highly-specific signal amplification, novel hydrogel-tissue chemistry, and an error-reduction sequencing process. The capabilities of STARmap were tested by mapping from 160 to 1,020 distinct genes simultaneously in sections of mouse brain at single-cell resolution with unprecedented efficiency, accuracy and reproducibility. These experiments led to the discovery of multiple new neocortical cell types, with gene markers and spatial patterns of organization not previously described, by comparison of the molecularly-defined architectures of sensory versus cognitive neocortex, and by quantification of expression of activity-regulated genes as a function of stimulation condition, spatial position, and cell typology. By adapting STARmap to thick tissue blocks, we observed and confirmed a novel molecularly-defined gradient distribution of excitatory neuron subtypes across cubic millimeter-scale volumes (>30,000 cells), and discovered a short-range 3D pattern of self-clustering shared by many inhibitory neuron subtypes that was accurately identifiable with a 3D STARmap approach. 

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2. Leveraging Single-Cell Populations to Uncover the Genetic Basis of Complex Traits

   https://doi.org/10.1146/annurev-genet-022123-110824  

   Minow, Mark A.A.; Marand, Alexandre P.; Schmitz, Robert J. (November 2023, Annual Review of Genetics)

   The ease and throughput of single-cell genomics have steadily improved, and its current trajectory suggests that surveying single-cell populations will become routine. We discuss the merger of quantitative genetics with single-cell genomics and emphasize how this synergizes with advantages intrinsic to plants. Single-cell population genomics provides increased detection resolution when mapping variants that control molecular traits, including gene expression or chromatin accessibility. Additionally, single-cell population genomics reveals the cell types in which variants act and, when combined with organism-level phenotype measurements, unveils which cellular contexts impact higher-order traits. Emerging technologies, notably multiomics, can facilitate the measurement of both genetic changes and genomic traits in single cells, enabling single-cell genetic experiments. The implementation of single-cell genetics will advance the investigation of the genetic architecture of complex molecular traits and provide new experimental paradigms to study eukaryotic genetics. 

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3. Single-cell transcriptomic analysis reveals diversity within mammalian spinal motor neurons

   https://doi.org/10.1038/s41467-022-35574-x  

   Liau, Ee Shan; Jin, Suoqin; Chen, Yen-Chung; Liu, Wei-Szu; Calon, Maëliss; Nedelec, Stéphane; Nie, Qing; Chen, Jun-An (January 2023, Nature Communications)

   Abstract Spinal motor neurons (MNs) integrate sensory stimuli and brain commands to generate movements. In vertebrates, the molecular identities of the cardinal MN types such as those innervating limb versus trunk muscles are well elucidated. Yet the identities of finer subtypes within these cell populations that innervate individual muscle groups remain enigmatic. Here we investigate heterogeneity in mouse MNs using single-cell transcriptomics. Among limb-innervating MNs, we reveal a diverse neuropeptide code for delineating putative motor pool identities. Additionally, we uncover that axial MNs are subdivided into three molecularly distinct subtypes, defined by mediolaterally-biased Satb2, Nr2f2 or Bcl11b expression patterns with different axon guidance signatures. These three subtypes are present in chicken and human embryos, suggesting a conserved axial MN expression pattern across higher vertebrates. Overall, our study provides a molecular resource of spinal MN types and paves the way towards deciphering how neuronal subtypes evolved to accommodate vertebrate motor behaviors. 

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4. Integrating EM and Patch-seq data: Synaptic connectivity and target specificity of predicted Sst transcriptomic types

   https://doi.org/10.1101/2023.03.22.533857  

   Gamlin, CR; Schneider-Mizell, CM; Mallory, M; Elabbady, L; Gouwens, N; Williams, G; Mukora, A; Dalley, R; Bodor, A; Brittain, D; et al (March 2023, bioRxiv)

   Abstract Neural circuit function is shaped both by the cell types that comprise the circuit and the connections between those cell types¹. Neural cell types have previously been defined by morphology^{2, 3}, electrophysiology^{4, 5}, transcriptomic expression^6–8, connectivity^9–13, or even a combination of such modalities^14–16. More recently, the Patch-seq technique has enabled the characterization of morphology (M), electrophysiology (E), and transcriptomic (T) properties from individual cells^17–20. Using this technique, these properties were integrated to define 28, inhibitory multimodal, MET-types in mouse primary visual cortex²¹. It is unknown how these MET-types connect within the broader cortical circuitry however. Here we show that we can predict the MET-type identity of inhibitory cells within a large-scale electron microscopy (EM) dataset and these MET-types have distinct ultrastructural features and synapse connectivity patterns. We found that EM Martinotti cells, a well defined morphological cell type^{22, 23}known to be Somatostatin positive (Sst+)^{24, 25}, were successfully predicted to belong to Sst+ MET-types. Each identified MET-type had distinct axon myelination patterns and synapsed onto specific excitatory targets. Our results demonstrate that morphological features can be used to link cell type identities across imaging modalities, which enables further comparison of connectivity in relation to transcriptomic or electrophysiological properties. Furthermore, our results show that MET-types have distinct connectivity patterns, supporting the use of MET-types and connectivity to meaningfully define cell types. 

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5. Light microscopy based approach for mapping connectivity with molecular specificity

   https://doi.org/10.1038/s41467-020-18422-8  

   Shen, Fred Y.; Harrington, Margaret M.; Walker, Logan A.; Cheng, Hon Pong Jimmy; Boyden, Edward S.; Cai, Dawen (September 2020, Nature Communications)

   Abstract Mapping neuroanatomy is a foundational goal towards understanding brain function. Electron microscopy (EM) has been the gold standard for connectivity analysis because nanoscale resolution is necessary to unambiguously resolve synapses. However, molecular information that specifies cell types is often lost in EM reconstructions. To address this, we devise a light microscopy approach for connectivity analysis of defined cell types called spectral connectomics. We combine multicolor labeling (Brainbow) of neurons with multi-round immunostaining Expansion Microscopy (miriEx) to simultaneously interrogate morphology, molecular markers, and connectivity in the same brain section. We apply this strategy to directly link inhibitory neuron cell types with their morphologies. Furthermore, we show that correlative Brainbow and endogenous synaptic machinery immunostaining can define putative synaptic connections between neurons, as well as map putative inhibitory and excitatory inputs. We envision that spectral connectomics can be applied routinely in neurobiology labs to gain insights into normal and pathophysiological neuroanatomy. 

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