Gonçalo Castelo-Branco

Bio: Gonçalo Castelo-Branco is an academic researcher from Karolinska Institutet. The author has contributed to research in topics: Oligodendrocyte & Chromatin. The author has an hindex of 28, co-authored 64 publications receiving 7084 citations. Previous affiliations of Gonçalo Castelo-Branco include Science for Life Laboratory & Wellcome Trust/Cancer Research UK Gurdon Institute.

Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq

Abstract: The mammalian cerebral cortex supports cognitive functions such as sensorimotor integration, memory, and social behaviors. Normal brain function relies on a diverse set of differentiated cell types, including neurons, glia, and vasculature. Here, we have used large-scale single-cell RNA sequencing (RNA-seq) to classify cells in the mouse somatosensory cortex and hippocampal CA1 region. We found 47 molecularly distinct subclasses, comprising all known major cell types in the cortex. We identified numerous marker genes, which allowed alignment with known cell types, morphology, and location. We found a layer I interneuron expressing Pax6 and a distinct postmitotic oligodendrocyte subclass marked by Itpr2. Across the diversity of cortical cell types, transcription factors formed a complex, layered regulatory code, suggesting a mechanism for the maintenance of adult cell type identity.

RNA velocity of single cells

Abstract: RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput1. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena such as embryogenesis or tissue regeneration. Here we show that RNA velocity-the time derivative of the gene expression state-can be directly estimated by distinguishing between unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.

Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system.

Abstract: Oligodendrocytes have been considered as a functionally homogeneous population in the central nervous system (CNS). We performed single-cell RNA sequencing on 5072 cells of the oligodendrocyte lineage from 10 regions of the mouse juvenile and adult CNS. Thirteen distinct populations were identified, 12 of which represent a continuum from Pdgfra(+) oligodendrocyte precursor cells (OPCs) to distinct mature oligodendrocytes. Initial stages of differentiation were similar across the juvenile CNS, whereas subsets of mature oligodendrocytes were enriched in specific regions in the adult brain. Newly formed oligodendrocytes were detected in the adult CNS and were responsive to complex motor learning. A second Pdgfra(+) population, distinct from OPCs, was found along vessels. Our study reveals the dynamics of oligodendrocyte differentiation and maturation, uncoupling them at a transcriptional level and highlighting oligodendrocyte heterogeneity in the CNS.

Altered human oligodendrocyte heterogeneity in multiple sclerosis

Abstract: Oligodendrocyte pathology is increasingly implicated in neurodegenerative diseases as oligodendrocytes both myelinate and provide metabolic support to axons. In multiple sclerosis (MS), demyelination in the central nervous system thus leads to neurodegeneration, but the severity of MS between patients is very variable. Disability does not correlate well with the extent of demyelination1, which suggests that other factors contribute to this variability. One such factor may be oligodendrocyte heterogeneity. Not all oligodendrocytes are the same—those from the mouse spinal cord inherently produce longer myelin sheaths than those from the cortex2, and single-cell analysis of the mouse central nervous system identified further differences3,4. However, the extent of human oligodendrocyte heterogeneity and its possible contribution to MS pathology remain unknown. Here we performed single-nucleus RNA sequencing from white matter areas of post-mortem human brain from patients with MS and from unaffected controls. We identified subclusters of oligodendroglia in control human white matter, some with similarities to mouse, and defined new markers for these cell states. Notably, some subclusters were underrepresented in MS tissue, whereas others were more prevalent. These differences in mature oligodendrocyte subclusters may indicate different functional states of oligodendrocytes in MS lesions. We found similar changes in normal-appearing white matter, showing that MS is a more diffuse disease than its focal demyelination suggests. Our findings of an altered oligodendroglial heterogeneity in MS may be important for understanding disease progression and developing therapeutic approaches. Single-nucleus RNA sequencing analysis identifies different subclusters of oligodendroglia in white matter from individuals with multiple sclerosis compared with controls, and these differences may be important for understanding disease progression.

Differential regulation of midbrain dopaminergic neuron development by Wnt-1, Wnt-3a, and Wnt-5a.

Abstract: The Wnts are a family of glycoproteins that regulate cell proliferation, fate decisions, and differentiation. In our study, we examined the contribution of Wnts to the development of ventral midbrain (VM) dopaminergic (DA) neurons. Our results show that β-catenin is expressed in DA precursor cells and that β-catenin signaling takes place in these cells, as assessed in TOPGAL [Tcf optimal-promoter β-galactosidase] reporter mice. We also found that Wnt-1, -3a, and -5a expression is differentially regulated during development and that partially purified Wnts distinctively regulate VM development. Wnt-3a promoted the proliferation of precursor cells expressing the orphan nuclear receptor-related factor 1 (Nurr1) but did not increase the number of tyrosine hydroxylase-positive neurons. Instead, Wnt-1 and -5a increased the number of rat midbrain DA neurons in rat embryonic day 14.5 precursor cultures by two distinct mechanisms. Wnt-1 predominantly increased the proliferation of Nurr1+ precursors, up-regulated cyclins D1 and D3, and down-regulated p27 and p57 mRNAs. In contrast, Wnt-5a primarily increased the proportion of Nurr1+ precursors that acquired a neuronal DA phenotype and up-regulated the expression of Ptx3 and c-ret mRNA. Moreover, the soluble cysteine-rich domain of Frizzled-8 (a Wnt inhibitor) blocked endogenous Wnts and the effects of Wnt-1 and -5a on proliferation and the acquisition of a DA phenotype in precursor cultures. These findings indicate that Wnts are key regulators of proliferation and differentiation of DA precursors during VM neurogenesis and that different Wnts have specific and unique activity profiles.

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Fast, sensitive and accurate integration of single-cell data with Harmony.

Abstract: The emerging diversity of single-cell RNA-seq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies, because biological and technical differences are interspersed. We present Harmony ( https://github.com/immunogenomics/harmony ), an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms while requiring fewer computational resources. Harmony enables the integration of ~106 cells on a personal computer. We apply Harmony to peripheral blood mononuclear cells from datasets with large experimental differences, five studies of pancreatic islet cells, mouse embryogenesis datasets and the integration of scRNA-seq with spatial transcriptomics data. Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.