Comprehensive Integration of Single-Cell Data.

Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

/pdf/integrating-single-cell-transcriptomic-data-across-different-3dtsi5w7ob.pdf

Integrating single-cell transcriptomic data across different conditions, technologies, and species.

Single cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we develop a computational strategy to "anchor" diverse datasets together, enabling us to integrate and compare single cell measurements not only across scRNA-seq technologies, but different modalities as well. After demonstrating substantial improvement over existing methods for data integration, we anchor scRNA-seq experiments with scATAC-seq datasets to explore chromatin differences in closely related interneuron subsets, and project single cell protein measurements onto a human bone marrow atlas to annotate and characterize lymphocyte populations. Lastly, we demonstrate how anchoring can harmonize in-situ gene expression and scRNA-seq datasets, allowing for the transcriptome-wide imputation of spatial gene expression patterns, and the identification of spatial relationships between mapped cell types in the visual cortex. Our work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets. Availability: Installation instructions, documentation, and tutorials are available at: https://www.satijalab.org/seurat

https://biorxiv.org/content/biorxiv/early/2018/11/02/460147.full-text.pdf

Comprehensive integration of single cell data

Mammalian organogenesis is a remarkable process. Within a short timeframe, the cells of the three germ layers transform into an embryo that includes most of the major internal and external organs. Here we investigate the transcriptional dynamics of mouse organogenesis at single-cell resolution. Using single-cell combinatorial indexing, we profiled the transcriptomes of around 2 million cells derived from 61 embryos staged between 9.5 and 13.5 days of gestation, in a single experiment. The resulting ‘mouse organogenesis cell atlas’ (MOCA) provides a global view of developmental processes during this critical window. We use Monocle 3 to identify hundreds of cell types and 56 trajectories, many of which are detected only because of the depth of cellular coverage, and collectively define thousands of corresponding marker genes. We explore the dynamics of gene expression within cell types and trajectories over time, including focused analyses of the apical ectodermal ridge, limb mesenchyme and skeletal muscle. Data from single-cell combinatorial-indexing RNA-sequencing analysis of 2 million cells from mouse embryos between embryonic days 9.5 and 13.5 are compiled in a cell atlas of mouse organogenesis, which provides a global view of developmental processes occurring during this critical period.

The single-cell transcriptional landscape of mammalian organogenesis

A Completely Reimplemented MPI Bioinformatics Toolkit with a New HHpred Server at its Core

Diverse subsets of cortical interneurons have vital roles in higher-order brain functions. To investigate how this diversity is generated, here we used single-cell RNA sequencing to profile the transcriptomes of mouse cells collected along a developmental time course. Heterogeneity within mitotic progenitors in the ganglionic eminences is driven by a highly conserved maturation trajectory, alongside eminence-specific transcription factor expression that seeds the emergence of later diversity. Upon becoming postmitotic, progenitors diverge and differentiate into transcriptionally distinct states, including an interneuron precursor state. By integrating datasets across developmental time points, we identified shared sources of transcriptomic heterogeneity between adult interneurons and their precursors, and uncovered the embryonic emergence of cardinal interneuron subtypes. Our analysis revealed that the transcription factor Mef2c, which is linked to various neuropsychiatric and neurodevelopmental disorders, delineates early precursors of parvalbumin-expressing neurons, and is essential for their development. These findings shed new light on the molecular diversification of early inhibitory precursors, and identify gene modules that may influence the specification of human interneuron subtypes.

/pdf/developmental-diversification-of-cortical-inhibitory-54qggdew18.pdf

Developmental diversification of cortical inhibitory interneurons.

Puberty onset is initiated by activation of neurons that secrete gonadotropin-releasing hormone (GnRH). The timing and progression of puberty may depend upon temporal coordination of two opposing central mechanisms—a restraint of GnRH secretion before puberty onset, followed by enhanced stimulation of GnRH release to complete reproductive maturation during puberty. Neuronal estrogen receptor α (ERα) has been implicated in both controls; however, the underlying neural circuits are not well understood. Here we test whether these mechanisms are mediated by neurons that express kisspeptin, a neuropeptide that modulates GnRH neurosecretion. Strikingly, conditional ablation of ERα in kisspeptin neurons results in a dramatic advancement of puberty onset in female mice. Furthermore, subsequent pubertal maturation is arrested in these animals, as they fail to acquire normal ovulatory cyclicity. We show that the temporal coordination of juvenile restraint and subsequent pubertal activation is likely mediated by ERα in two separate kisspeptin neuronal populations in the hypothalamus.

https://www.pnas.org/content/pnas/107/52/22693.full.pdf

Timing and completion of puberty in female mice depend on estrogen receptor α-signaling in kisspeptin neurons

The MPI Bioinformatics Toolkit is an interactive web service which offers access to a great variety of public and in-house bioinformatics tools. They are grouped into different sections that support sequence searches, multiple alignment, secondary and tertiary structure prediction and classification. Several public tools are offered in customized versions that extend their functionality. For example, PSI-BLAST can be run against regularly updated standard databases, customized user databases or selectable sets of genomes. Another tool, Quick2D, integrates the results of various secondary structure, transmembrane and disorder prediction programs into one view. The Toolkit provides a friendly and intuitive user interface with an online help facility. As a key feature, various tools are interconnected so that the results of one tool can be forwarded to other tools. One could run PSI-BLAST, parse out a multiple alignment of selected hits and send the results to a cluster analysis tool. The Toolkit framework and the tools developed in-house will be packaged and freely available under the GNU Lesser General Public Licence (LGPL). The Toolkit can be accessed at http://toolkit.tuebingen.mpg.de.

http://www.soeding.genzentrum.lmu.de/assets/Lab-Soeding/Lab-Soeding-Publications/NAR-2006-Biegert-MPI-toolkit.pdf

The MPI Bioinformatics Toolkit for protein sequence analysis

Puberty onset is initiated in the brain by activation of gonadotropin-releasing hormone (GnRH) neurosecretion. Different permissive signals must be integrated for the initiation of reproductive maturation; however, the neural circuits controlling timely awakening of the reproductive axis are not understood. The identification of the neuropeptide kisspeptin as a potent activator of GnRH neuronal activity suggests that kisspeptin-releasing neurons might coordinate puberty onset. To test this hypothesis, we generated mice that specifically lack kisspeptin cells. Puberty onset in females was unaffected by kisspeptin neuron ablation. Furthermore, the animals were fertile, albeit with smaller ovaries. Consistent with this, female mice lacking neurons that express the kisspeptin receptor GPR54 were also fertile. Acute ablation of kisspeptin neurons in adult mice inhibited fertility, suggesting that there is compensation for the loss of kisspeptin neurons early in development. Our data indicate that the initiation and completion of reproductive maturation can occur in the absence of kisspeptin/GPR54 signaling.

Female reproductive maturation in the absence of kisspeptin/GPR54 signaling.

Motivation: The prediction of ligand-binding residues or catalytically active residues of a protein may give important hints that can guide further genetic or biochemical studies. Existing sequence-based prediction methods mostly rank residue positions by evolutionary conservation calculated from a multiple sequence alignment of homologs. A problem hampering more wide-spread application of these methods is the low per-residue precision, which at 20% sensitivity is around 35% for ligand-binding residues and 20% for catalytic residues.

Results: We combine information from the conservation at each site, its amino acid distribution, as well as its predicted secondary structure (ss) and relative solvent accessibility (rsa). First, we measure conservation by how much the amino acid distribution at each site differs from the distribution expected for the predicted ss and rsa states. Second, we include the conservation of neighboring residues in a weighted linear score by analytically optimizing the signal-to-noise ratio of the total score. Third, we use conditional probability density estimation to calculate the probability of each site to be functional given its conservation, the observed amino acid distribution, and the predicted ss and rsa states.

We have constructed two large data sets, one based on the Catalytic Site Atlas and the other on PDB SITE records, to benchmark methods for predicting functional residues. The new method FRcons predicts ligand-binding and catalytic residues with higher precision than alternative methods over the entire sensitivity range, reaching 50% and 40% precision at 20% sensitivity, respectively.

Availability: Server: http://frpred.tuebingen.mpg.de. Data sets: ftp://ftp.tuebingen.mpg.de/pub/protevo/FRpred/

Contact: soeding@lmb.uni-muenchen.de

Supplementary information: Supplementary data are available at Bioinformatics Online.

Christian Mayer

Papers

Developmental diversification of cortical inhibitory interneurons.

Timing and completion of puberty in female mice depend on estrogen receptor α-signaling in kisspeptin neurons

The MPI Bioinformatics Toolkit for protein sequence analysis

Female reproductive maturation in the absence of kisspeptin/GPR54 signaling.

Prediction of protein functional residues from sequence by probability density estimation