Neuronal atlas of the dorsal horn defines its architecture and links sensory input to transcriptional cell types.

Nat Neurosci.

2018 Apr 23

Häring M, Zeisel A, Hochgerner H, Rinwa P, Jakobsson JET, Lönnerberg P, La Manno G, Sharma N, Borgius L, Kiehn O, Lagerström MC, Linnarsson S, Ernfors P.
PMID: 29686262 | DOI: 10.1038/s41593-018-0141-1

The dorsal horn of the spinal cord is critical to processing distinct modalities of noxious and innocuous sensation, but little is known of the neuronal subtypes involved, hampering efforts to deduce principles governing somatic sensation. Here we used single-cell RNA sequencing to classify sensory neurons in the mouse dorsal horn. We identified 15 inhibitory and 15 excitatory molecular subtypes of neurons, equaling the complexity in cerebral cortex. Validating our classification scheme in vivo and matching cell types to anatomy of the dorsal horn by spatial transcriptomics reveals laminar enrichment for each of the cell types. Neuron types, when combined, define a multilayered organization with like neurons layered together. Employing our scheme, we find that heat and cold stimuli activate discrete sets of both excitatory and inhibitory neuron types. This work provides a systematic and comprehensive molecular classification of spinal cord sensory neurons, enabling functional interrogation of sensory processing.

Shared and distinct transcriptomic cell types across neocortical areas.

Nature. 2018 Nov;563(7729):72-78.

2018 Oct 31

Tasic B, Yao Z, Graybuck LT, Smith KA, Nguyen TN, Bertagnolli D, Goldy J, Garren E, Economo MN, Viswanathan S, Penn O, Bakken T, Menon V, Miller J, Fong O, Hirokawa KE, Lathia K, Rimorin C, Tieu M, Larsen R, Casper T, Barkan E, Kroll M, Parry S, Shapovalova NV, Hirschstein D, Pendergraft J, Sullivan HA, Kim TK, Szafer A, Dee N, Groblewski P, Wickersham I, Cetin A, Harris JA, Levi BP, Sunkin SM, Madisen L, Daigle TL, Looger L, Bernard A, Phillips J, Lein E, Hawrylycz M, Svoboda K, Jones AR, Koch C, Zeng H.
PMID: 30382198 | DOI: 10.1038/s41586-018-0654-5

The neocortex contains a multitude of cell types that are segregated into layers and functionally distinct areas. To investigate the diversity of cell types across the mouse neocortex, here we analysed 23,822 cells from two areas at distant poles of the mouse neocortex: the primary visual cortex and the anterior lateral motor cortex. We define 133 transcriptomic cell types by deep, single-cell RNA sequencing. Nearly all types of GABA (gamma-aminobutyric acid)-containing neurons are shared across both areas, whereas most types of glutamatergic neurons were found in one of the two areas. By combining single-cell RNA sequencing and retrograde labelling, we match transcriptomic types of glutamatergic neurons to their long-range projection specificity. Our study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex.

Shared and distinct transcriptomic cell types across neocortical areas.

Nature.

2018 Oct 31

The neocortex contains a multitude of cell types that are segregated into layers and functionally distinct areas. To investigate the diversity of cell types across the mouse neocortex, here we analysed 23,822 cells from two areas at distant poles of the mouse neocortex: the primary visual cortex and the anterior lateral motor cortex. We define 133 transcriptomic cell types by deep, single-cell RNA sequencing. Nearly all types of GABA (γ-aminobutyric acid)-containing neurons are shared across both areas, whereas most types of glutamatergic neurons were found in one of the two areas. By combining single-cell RNA sequencing and retrograde labelling, we match transcriptomic types of glutamatergic neurons to their long-range projection specificity. Our study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex.

Single-Cell Transcriptomic Analysis of Human Lung Provides Insights into the Pathobiology of Pulmonary Fibrosis.

Am J Respir Crit Care Med. 2018 Dec 15.

2018 Dec 15

Reyfman PA, Walter JM, Joshi N, Anekalla KR, McQuattie-Pimentel AC, Chiu S, Fernandez R, Akbarpour M, Chen CI, Ren Z, Verma R, Abdala-Valencia H, Nam K, Chi M, Han S, Gonzalez-Gonzalez FJ, Soberanes S, Watanabe S, Williams KJN, Flozak AS, Nicholson TT, Morgan VK, Winter DR, Hinchcliff M, Hrusch CL, Guzy RD, Bonham CA, Sperling AI, Bag R, Hamanaka RB, Mutlu GM, Yeldandi AV, Marshall SA, Shilatifard A, Amaral LAN, Perlman H, Sznajder JI, Argento AC, Gillespie CT, Dematte J, Jain M, Singer BD, Ridge KM, Lam AP, Bharat A, Bhorade SM, Gottardi CJ, Budinger GRS, Misharin AV.
PMID: 30554520 | DOI: 10.1164/rccm.201712-2410OC

Abstract RATIONALE: The contributions of diverse cell populations in the human lung to pulmonary fibrosis pathogenesis are poorly understood. Single-cell RNA sequencing can reveal changes within individual cell populations during pulmonary fibrosis that are important for disease pathogenesis. OBJECTIVES: To determine whether single-cell RNA sequencing can reveal disease-related heterogeneity within alveolar macrophages, epithelial cells or other cell types in lung tissue from subjects with pulmonary fibrosis compared with controls. METHODS: We performed single-cell RNA sequencing on lung tissue obtained from eight transplant donors and eight recipients with pulmonary fibrosis and on one bronchoscopic cryobiospy sample from a patient with idiopathic pulmonary fibrosis. We validated these data in using in situ RNA hybridization, immunohistochemistry, and bulk RNA-sequencing on flow-sorted cells from 22 additional subjects. MEASUREMENTS AND MAIN RESULTS: We identified a distinct, novel population of profibrotic alveolar macrophages exclusively in patients with fibrosis. Within epithelial cells, the expression of genes involved in Wnt secretion and response was restricted to non-overlapping cells. We identified rare cell populations including airway stem cells and senescent cells emerging during pulmonary fibrosis. We developed a web-based tool to explore these data. CONCLUSIONS: We generated a single cell atlas of pulmonary fibrosis. Using this atlas we demonstrated heterogeneity within alveolar macrophages and epithelial cells from subjects with pulmonary fibrosis. These results support the feasibility of discovery-based approaches using next generation sequencing technologies to identify signaling pathways for targeting in the development of personalized therapies for patients with pulmonary fibrosis.

	Description
sense Example: Hs-LAG3-sense	Standard probes for RNA detection are in antisense. Sense probe is reverse complent to the corresponding antisense probe.
Intron# Example: Mm-Htt-intron2	Probe targets the indicated intron in the target gene, commonly used for pre-mRNA detection
Pool/Pan Example: Hs-CD3-pool (Hs-CD3D, Hs-CD3E, Hs-CD3G)	A mixture of multiple probe sets targeting multiple genes or transcripts
No-XSp Example: Hs-PDGFB-No-XMm	Does not cross detect with the species (Sp)
XSp Example: Rn-Pde9a-XMm	designed to cross detect with the species (Sp)
O# Example: Mm-Islr-O1	Alternative design targeting different regions of the same transcript or isoforms
CDS Example: Hs-SLC31A-CDS	Probe targets the protein-coding sequence only
EnEm	Probe targets exons n and m
En-Em	Probe targets region from exon n to exon m
Retired Nomenclature
tvn Example: Hs-LEPR-tv1	Designed to target transcript variant n
ORF Example: Hs-ACVRL1-ORF	Probe targets open reading frame
UTR Example: Hs-HTT-UTR-C3	Probe targets the untranslated region (non-protein-coding region) only
5UTR Example: Hs-GNRHR-5UTR	Probe targets the 5' untranslated region only
3UTR Example: Rn-Npy1r-3UTR	Probe targets the 3' untranslated region only
Pan Example: Pool	A mixture of multiple probe sets targeting multiple genes or transcripts

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