Cell2location maps fine-grained cell types in spatial transcriptomics

2022 ◽  
Author(s):  
Vitalii Kleshchevnikov ◽  
Artem Shmatko ◽  
Emma Dann ◽  
Alexander Aivazidis ◽  
Hamish W. King ◽  
...  
Keyword(s):  
Cell Types ◽  
Fine Grained

scholarly journals CellMap: Characterizing the types and composition of iPSC-derived cells from RNA-seq data

2021 ◽  
Author(s):  
Zhengyu Ouyang ◽  
Nathanael Bourgeois ◽  
Eugenia Lyashenko ◽  
Paige Cundiff ◽  
Patrick F Cullen ◽  
...  
Keyword(s):  
Single Cell ◽  
Induced Pluripotent Stem Cell ◽  
Cell Types ◽  
Model Systems ◽  
Rna Seq ◽  
Cell Type ◽  
Fine Grained ◽  
Single Nucleus ◽  
Induced Pluripotent

Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines.

Delta-Notch signalling and the patterning of sensory cell differentiation in the zebrafish ear: evidence from the mind bomb mutant

Development ◽  
1998 ◽  
Vol 125 (23) ◽  
pp. 4637-4644 ◽  
Author(s):  
C. Haddon ◽  
Y.J. Jiang ◽  
L. Smithers ◽  
J. Lewis
Keyword(s):  
Cell Differentiation ◽  
Lateral Inhibition ◽  
Hair Cells ◽  
Sensory Cell ◽  
Cell Types ◽  
Notch Signalling ◽  
Supporting Cells ◽  
Fine Grained ◽  
Mechanosensory Hair Cells ◽  
The Mind

Mechanosensory hair cells in the sensory patches of the vertebrate ear are interspersed among supporting cells, forming a fine-grained pattern of alternating cell types. Analogies with Drosophila mechanosensory bristle development suggest that this pattern could be generated through lateral inhibition mediated by Notch signalling. In the zebrafish ear rudiment, homologues of Notch are widely expressed, while the Delta homologues deltaA, deltaB and deltaD, coding for Notch ligands, are expressed in small numbers of cells in regions where hair cells are soon to differentiate. This suggests that the delta-expressing cells are nascent hair cells, in agreement with findings for Delta1 in the chick. According to the lateral inhibition hypothesis, the nascent hair cells, by expressing Delta protein, would inhibit their neighbours from becoming hair cells, forcing them to be supporting cells instead. The zebrafish mind bomb mutant has abnormalities in the central nervous system, somites, and elsewhere, diagnostic of a failure of Delta-Notch signalling: in the CNS, it shows a neurogenic phenotype accompanied by misregulated delta gene expression. Similar misregulation of delta; genes is seen in the ear, along with misregulation of a Serrate homologue, serrateB, coding for an alternative Notch ligand. Most dramatically, the sensory patches in the mind bomb ear consist solely of hair cells, which are produced in great excess and prematurely; at 36 hours post fertilization, there are more than ten times as many as normal, while supporting cells are absent. A twofold increase is seen in the number of otic neurons also. The findings are strong evidence that lateral inhibition mediated by Delta-Notch signalling controls the pattern of sensory cell differentiation in the ear.

scholarly journals Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data with ImmClassifier

2020 ◽  
Author(s):  
Xuan Liu ◽  
Sara J.C. Gosline ◽  
Lance T. Pflieger ◽  
Pierre Wallet ◽  
Archana Iyer ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Cell Types ◽  
Effector Memory ◽  
Immune Cell Population ◽  
Fine Grained ◽  
Knowledge Based ◽  
Comparable Performance

AbstractSingle-cell RNA sequencing is an emerging strategy for characterizing the immune cell population in diverse environments including blood, tumor or healthy tissues. While this has traditionally been done with flow or mass cytometry targeting protein expression, scRNA-Seq has several established and potential advantages in that it can profile immune cells and non-immune cells (e.g. cancer cells) in the same sample, identify cell types that lack precise markers for flow cytometry, or identify a potentially larger number of immune cell types and activation states than is achievable in a single flow assay. However, scRNA-Seq is currently limited due to the need to identify the types of each immune cell from its transcriptional profile, which is not only time-consuming but also requires a significant knowledge of immunology. While recently developed algorithms accurately annotate coarse cell types (e.g. T cells vs macrophages), making fine distinctions has turned out to be a difficult challenge. To address this, we developed a machine learning classifier called ImmClassifier that leverages a hierarchical ontology of cell type. We demonstrate that ImmClassifier outperforms other tools (+20% recall, +14% precision) in distinguishing fine-grained cell types (e.g. CD8+ effector memory T cells) with comparable performance on coarse ones. Thus, ImmClassifier can be used to explore more deeply the heterogeneity of the immune system in scRNA-Seq experiments.

scholarly journals Simultaneous production of diverse neuronal subtypes during early corticogenesis

2018 ◽  
Author(s):  
E. Magrinelli ◽  
R. J. Wagener ◽  
D. Jabaudon
Keyword(s):  
Deep Layer ◽  
Ventricular Zone ◽  
Neuronal Cell ◽  
Cell Types ◽  
Superficial Layer ◽  
High Temporal Resolution ◽  
Fine Grained ◽  
Molecular Identity ◽  
Deep Layers ◽  
Successive Generations

AbstractThe circuits of the neocortex are composed of a broad diversity of neuronal cell types, which can be distinguished by their laminar location, molecular identity, and connectivity. During embryogenesis, successive generations of glutamatergic neurons are sequentially born from progenitors located in germinal zones below the cortex. In this process, the earliest-born generations of neurons differentiate to reside in deep layers, while later-born daughter neurons reside in more superficial layers. Although the aggregate competence of progenitors to produce successive subtypes of neurons progresses as corticogenesis proceeds, a fine-grained temporal understanding of how neuronal subtypes are sequentially produced is still missing. Here, we use FlashTag, a high temporal resolution labeling approach, to follow the fate of the simultaneously-born daughter neurons of ventricular zone progenitors at multiple stages of corticogenesis. Our findings reveal a bimodal regulation in the diversity of neurons being produced at single time points of corticogenesis. Initially, distinct subtypes of deep-layer neurons are simultaneously produced, as defined by their laminar location, molecular identity and connectivity. Later on, instead, instantaneous neuronal production is homogeneous and the distinct superficial-layer neurons subtypes are sequentially produced. These findings suggest that early-born, deep-layer neurons have a less determined fate potential than later-born superficial layer neurons, which may reflect the progressive implementation of pre-and/or post-mitotic mechanisms controlling neuronal fate reliability.

scholarly journals ГЕМОЦИТИ ІНТАКТНОЇ ТА ІНВАЗОВАНОЇ ТРЕМАТОДАМИ ЖАБУРНИЦІ КИТАЙСЬКОЇ (MOLLUSCA, UNIONIDAE, ANODONTINAE)

2019 ◽  
Vol 75 (1) ◽  
pp. 87-93 ◽  
Author(s):  
A. P. Stadnychenko ◽  
O. I. Uvaeva ◽  
D. A. Vyskushenko ◽  
O. D. Shimkovich
Keyword(s):  
Infection Intensity ◽  
Cell Types ◽  
Pathological Process ◽  
Focal Lesions ◽  
Protective Function ◽  
Fine Grained ◽  
Black Sea Coast ◽  
Eosinophilic Granulocytes ◽  
Cambial Cells ◽  
Host Tissues

The hemolymph of Sinanodonta woodiana (Lea, 1834) consists of plasma and cells of four types: prohemocytes (cambial cells), macronucleocytes, basophilic and eosinophilic granulocytes. The three latter cell types derive from the cambial cells though mitosis. The cellular sizes are: prohemocytes 14.3±0.4, basophilic granulocytes 20.9±0.7, young eosinophilic granulocytes 23.8±0.6, older eosinophilic granulocytes 25.7±0.1, macronucleocytes 26.1±0.3. All cells and their nuclei are roundish. Nuclear chromatin is either fine-grained fairly evenly distributed in the karyoplasm (in basophilic granulocytes), or more or less grouped dark-colored small (2-5-6) chromatin blocks. NC-ratio is maximum in macronucleocytes (0.6±0.01). NC-ratio of prohemocytes is 0.4±0.01, that of basophilic granulocytes is 0.3±0.01. NC-ratio of eosinophilic granulocytes is 0.2±0.01. Eosinophilic cells prevail in hemolymph elements: the young eosinophilic granulocytes make up 25.1±0.4 % and the older eosinophilic cells are up to 27.9±0.6 % of all hemocytes. The major functions of hemolymph cells are transport and protection.Transport is particularly pronounced in basophilic granulocytes in the form of phagocytosis. The protective function of hemocytes of different categories is manifested in a different way. Thus, some of the basophilic granulocytes develop into nephrocytes, which accumulate numerous (11-23) vacuoles of yellow-green-brown color. The vacuoles are subsequently excreted by the mollusk through the kidneys. The protective function of eosinophilic granulocytes is realized as false agglutination (these hemocytes clog into lumps that close wounds), which helps preventing blood loss. Also, eosinophilic granulocytes merge into multinucleated plasmodium, accumulating in large quantities around various foreign bodies (parasites or fragments of destroyed tissues) and encapsulating them to isolate from host tissues. At Northern Black Sea Coast, Chinese pond mussel is a common intermediate host of the trematode Rhipidocotyle companula Dujardin, 1845.The trematode inhabits the mollusk’s gonads. The parasitic sporocysts and cercariae were found in 29 % of examined mollusk specimens. Infestations were weak (up to 10 % of gonads were affected) in 22.4 % of infected mollusks, moderate (10 to 50 % of gonads) in 70 % of contaminated mollusks. Only 7.6 % of infected pond mussels were hyperinfected (100 % of gonads were affected).Weak trematode infestation is accompanied by localized damage. The total number and volume of parasitic focal lesions are generally insignificant. Moderate infection, and especially a hyperinfection cause not only the lesions in the hostal biotope, but also the overall pathological process in mollusk hosts. Simultaneously, the prohemocytes and basophilic granulocytes as well as their nuclei reduce in size. The total number of prohemocytes also declines by 1.7 times. The greatest decrease in the nuclear-cytoplasmic ratio (by a factor of 1.5-2) is noted also for prohemocytes and basophilic granulocytes. The vacuolization of karyoplasm and cytoplasm, the basophilization of cytoplasm, the degenerative changes in hemocyte nuclei (karyopicnosis, karyorexis, karyolysis), and the total number of aging and dying  hemocytes are directly related to the level of infection intensity.

scholarly journals Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data

2021 ◽  
Author(s):  
Xuan Liu ◽  
Sara J C Gosline ◽  
Lance T Pflieger ◽  
Pierre Wallet ◽  
Archana Iyer ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Immune Cell ◽  
Cell Types ◽  
Effector Memory ◽  
Fine Grained ◽  
Knowledge Based ◽  
Learning Classifier ◽  
Comparable Performance ◽  
Difficult Challenge

Abstract Single-cell RNA sequencing (scRNA-Seq) is an emerging strategy for characterizing immune cell populations. Compared to flow or mass cytometry, scRNA-Seq could potentially identify cell types and activation states that lack precise cell surface markers. However, scRNA-Seq is currently limited due to the need to manually classify each immune cell from its transcriptional profile. While recently developed algorithms accurately annotate coarse cell types (e.g. T cells versus macrophages), making fine distinctions (e.g. CD8+ effector memory T cells) remains a difficult challenge. To address this, we developed a machine learning classifier called ImmClassifier that leverages a hierarchical ontology of cell type. We demonstrate that its predictions are highly concordant with flow-based markers from CITE-seq and outperforms other tools (+15% recall, +14% precision) in distinguishing fine-grained cell types with comparable performance on coarse ones. Thus, ImmClassifier can be used to explore more deeply the heterogeneity of the immune system in scRNA-Seq experiments.

scholarly journals Using equivalence class counts for fast and accurate testing of differential transcript usage

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 265 ◽  
Author(s):  
Marek Cmero ◽  
Nadia M. Davidson ◽  
Alicia Oshlack
Keyword(s):  
Equivalence Class ◽  
Cell Types ◽  
Lower Sensitivity ◽  
Rna Seq ◽  
Experimental Conditions ◽  
False Discovery Rates ◽  
Fine Grained ◽  
Transcript Quantification ◽  
Exon Level ◽  
Usage Analysis

Background: RNA sequencing has enabled high-throughput and fine-grained quantitative analyses of the transcriptome. While differential gene expression is the most widely used application of this technology, RNA-seq data also has the resolution to infer differential transcript usage (DTU), which can elucidate the role of different transcript isoforms between experimental conditions, cell types or tissues. DTU has typically been inferred from exon-count data, which has issues with assigning reads unambiguously to counting bins, and requires alignment of reads to the genome. Recently, approaches have emerged that use transcript quantification estimates directly for DTU. Transcript counts can be inferred from 'pseudo' or lightweight aligners, which are significantly faster than traditional genome alignment. However, recent evaluations show lower sensitivity in DTU analysis compared to exon-level analysis. Transcript abundances are estimated from equivalence classes (ECs), which determine the transcripts that any given read is compatible with. Recent work has proposed performing a variety of RNA-seq analysis directly on equivalence class counts (ECCs). Methods: Here we demonstrate that ECCs can be used effectively with existing count-based methods for detecting DTU. We evaluate this approach on simulated human and drosophila data, as well as on a real dataset through subset testing. Results: We find that ECCs have similar sensitivity and false discovery rates as exon-level counts but can be generated in a fraction of the time through the use of pseudo-aligners. Conclusions: We posit that equivalence class read counts are a natural unit on which to perform differential transcript usage analysis.

scholarly journals Fast and accurate differential transcript usage by testing equivalence class counts

2018 ◽  
Author(s):  
Marek Cmero ◽  
Nadia M Davidson ◽  
Alicia Oshlack
Keyword(s):  
Equivalence Class ◽  
Cell Types ◽  
Lower Sensitivity ◽  
Experimental Conditions ◽  
False Discovery Rates ◽  
Fine Grained ◽  
Exon Level ◽  
Quantitative Analyses ◽  
Differential Gene

AbstractRNA sequencing has enabled high-throughput and fine-grained quantitative analyses of the transcriptome. While differential gene expression is the most widely used application of this technology, RNA-seq data also has the resolution to infer differential transcript usage (DTU), which can elucidate the role of different transcript isoforms between experimental conditions, cell types or tissues. DTU has typically been inferred from exon-count data, which has issues with assigning reads unambiguously to counting bins, and requires alignment of reads to the genome. Recently, approaches have emerged that use transcript quantifications estimates directly for DTU. Transcript counts can be inferred from ‘pseudo’ or lightweight aligners, which are significantly faster than traditional genome alignment. However, recent evaluations show lower sensitivity in DTU analysis. Transcript abundances are estimated from equivalence classes (ECs), which determine the transcripts that any given read is compatible with. Here we propose performing DTU testing directly on equivalence class read counts. We evaluate this approach on simulated human and drosophila data, as well as on a real dataset through subset testing. We find that ECs counts have similar sensitivity and false discovery rates as exon-level counts but can be generated in a fraction of the time through the use of pseudo-aligners. We posit that equivalent class counts is a natural unit on which to perform many types of analysis.

scholarly journals Fast and accurate differential transcript usage by testing equivalence class counts

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 265
Author(s):  
Marek Cmero ◽  
Nadia M. Davidson ◽  
Alicia Oshlack
Keyword(s):  
Equivalence Class ◽  
Cell Types ◽  
Lower Sensitivity ◽  
Experimental Conditions ◽  
False Discovery Rates ◽  
Fine Grained ◽  
Exon Level ◽  
Quantitative Analyses ◽  
Differential Gene

Background: RNA sequencing has enabled high-throughput and fine-grained quantitative analyses of the transcriptome. While differential gene expression is the most widely used application of this technology, RNA-seq data also has the resolution to infer differential transcript usage (DTU), which can elucidate the role of different transcript isoforms between experimental conditions, cell types or tissues. DTU has typically been inferred from exon-count data, which has issues with assigning reads unambiguously to counting bins, and requires alignment of reads to the genome. Recently, approaches have emerged that use transcript quantifications estimates directly for DTU. Transcript counts can be inferred from 'pseudo' or lightweight aligners, which are significantly faster than traditional genome alignment. However, recent evaluations show lower sensitivity in DTU analysis. Transcript abundances are estimated from equivalence classes (ECs), which determine the transcripts that any given read is compatible with. Recent work has proposed performing differential expression testing directly on equivalence class read counts (ECs). Methods: Here we demonstrate that ECs can be used effectively with existing count-based methods for detecting DTU. We evaluate this approach on simulated human and drosophila data, as well as on a real dataset through subset testing. Results: We find that ECs counts have similar sensitivity and false discovery rates as exon-level counts but can be generated in a fraction of the time through the use of pseudo-aligners. Conclusions: We posit that equivalence class read counts are a natural unit on which to perform many types of analysis.

scholarly journals BABEL enables cross-modality translation between multiomic profiles at single-cell resolution

2021 ◽  
Vol 118 (15) ◽  
pp. e2023070118
Author(s):  
Kevin E. Wu ◽  
Kathryn E. Yost ◽  
Howard Y. Chang ◽  
James Zou
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Training Data ◽  
Grand Challenge ◽  
Widespread Application ◽  
Fine Grained ◽  
Cell Transcriptome ◽  
Cell Expression

Simultaneous profiling of multiomic modalities within a single cell is a grand challenge for single-cell biology. While there have been impressive technical innovations demonstrating feasibility—for example, generating paired measurements of single-cell transcriptome (single-cell RNA sequencing [scRNA-seq]) and chromatin accessibility (single-cell assay for transposase-accessible chromatin using sequencing [scATAC-seq])—widespread application of joint profiling is challenging due to its experimental complexity, noise, and cost. Here, we introduce BABEL, a deep learning method that translates between the transcriptome and chromatin profiles of a single cell. Leveraging an interoperable neural network model, BABEL can predict single-cell expression directly from a cell’s scATAC-seq and vice versa after training on relevant data. This makes it possible to computationally synthesize paired multiomic measurements when only one modality is experimentally available. Across several paired single-cell ATAC and gene expression datasets in human and mouse, we validate that BABEL accurately translates between these modalities for individual cells. BABEL also generalizes well to cell types within new biological contexts not seen during training. Starting from scATAC-seq of patient-derived basal cell carcinoma (BCC), BABEL generated single-cell expression that enabled fine-grained classification of complex cell states, despite having never seen BCC data. These predictions are comparable to analyses of experimental BCC scRNA-seq data for diverse cell types related to BABEL’s training data. We further show that BABEL can incorporate additional single-cell data modalities, such as protein epitope profiling, thus enabling translation across chromatin, RNA, and protein. BABEL offers a powerful approach for data exploration and hypothesis generation.

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