scholarly journals CytoGLMM: conditional differential analysis for flow and mass cytometry experiments

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Christof Seiler ◽  
Anne-Maud Ferreira ◽  
Lisa M. Kronstad ◽  
Laura J. Simpson ◽  
Mathieu Le Gars ◽  
...  
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Single Cells ◽  
Cell Types ◽  
Current Data ◽  
Model Specification ◽  
Differential Analysis ◽  
Mass Cytometry ◽  
Human Immunology ◽  
False Discoveries

Abstract Background Flow and mass cytometry are important modern immunology tools for measuring expression levels of multiple proteins on single cells. The goal is to better understand the mechanisms of responses on a single cell basis by studying differential expression of proteins. Most current data analysis tools compare expressions across many computationally discovered cell types. Our goal is to focus on just one cell type. Our narrower field of application allows us to define a more specific statistical model with easier to control statistical guarantees. Results Differential analysis of marker expressions can be difficult due to marker correlations and inter-subject heterogeneity, particularly for studies of human immunology. We address these challenges with two multiple regression strategies: a bootstrapped generalized linear model and a generalized linear mixed model. On simulated datasets, we compare the robustness towards marker correlations and heterogeneity of both strategies. For paired experiments, we find that both strategies maintain the target false discovery rate under medium correlations and that mixed models are statistically more powerful under the correct model specification. For unpaired experiments, our results indicate that much larger patient sample sizes are required to detect differences. We illustrate the R package and workflow for both strategies on a pregnancy dataset. Conclusion Our approach to finding differential proteins in flow and mass cytometry data reduces biases arising from marker correlations and safeguards against false discoveries induced by patient heterogeneity.

scholarly journals CytoGLMM: Conditional Differential Analysis for Flow and Mass Cytometry Experiments

2020 ◽  
Author(s):  
Christof Seiler ◽  
Anne-Maud Ferreira ◽  
Lisa M. Kronstad ◽  
Laura J. Simpson ◽  
Mathieu Le Gars ◽  
...  
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Single Cells ◽  
Cell Types ◽  
Model Specification ◽  
Differential Analysis ◽  
Mass Cytometry ◽  
Human Immunology ◽  
Fixed Cell ◽  
False Discoveries

AbstractBackgroundFlow and mass cytometry are important modern immunology tools for measuring expression levels of multiple proteins on single cells. The goal is to better understand the mechanisms of responses on a single cell basis by studying differential expression of proteins. We focus on cell-specific differential analysis and one fixed cell type. In contrast, most current methods learn cell types and perform differential analysis jointly. Our narrower field of application allows us to define a more specific statistical model with easier to control statistical guarantees.ResultsDifferential analysis of marker expressions can be difficult due to marker correlations and inter-individual heterogeneity, particularly for studies of human immunology. We address these challenges with two multiple regression strategies: A bootstrapped generalized linear model and a generalized linear mixed model. On simulated datasets, we compare the robustness towards marker correlations and heterogeneity of both strategies. For paired experiments, we find that both strategies maintain the target false discovery rate under medium correlations and that mixed models are statistically more powerful under the correct model specification. For unpaired experiments, our results indicate that much larger patient sample sizes are required to detect differences. We illustrate the CytoGLMMR package and workflow for both strategies on a pregnancy dataset.ConclusionsOur approach to find differential proteins in flow and mass cytometry data reduces biases arising from maker correlations and safeguards against false discoveries induced by patient heterogeneity.

scholarly journals Subcellular Specialization of Mitochondrial Form and Function in Skeletal Muscle Cells

2021 ◽  
Vol 9 ◽  
Author(s):  
T. Bradley Willingham ◽  
Peter T. Ajayi ◽  
Brian Glancy
Keyword(s):  
Skeletal Muscle ◽  
Energy Homeostasis ◽  
Single Cells ◽  
Muscle Cells ◽  
Cell Types ◽  
Current Data ◽  
Skeletal Muscle Cells ◽  
Form And Function ◽  
Mitochondrial Heterogeneity ◽  
And Function

Across different cell types and within single cells, mitochondria are heterogeneous in form and function. In skeletal muscle cells, morphologically and functionally distinct subpopulations of mitochondria have been identified, but the mechanisms by which the subcellular specialization of mitochondria contributes to energy homeostasis in working muscles remains unclear. Here, we discuss the current data regarding mitochondrial heterogeneity in skeletal muscle cells and highlight potential new lines of inquiry that have emerged due to advancements in cellular imaging technologies.

scholarly journals High-Dimensional Single-Cell Quantitative Profiling of Skeletal Muscle Cell Population Dynamics during Regeneration

Cells ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1723
Author(s):  
Lucia Lisa Petrilli ◽  
Filomena Spada ◽  
Alessandro Palma ◽  
Alessio Reggio ◽  
Marco Rosina ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Single Cells ◽  
Cell Types ◽  
Interstitial Space ◽  
Cell Populations ◽  
Wild Type ◽  
Mass Cytometry ◽  
Pathological Conditions ◽  
Comprehensive Picture ◽  
Quantitative Profiling

The interstitial space surrounding the skeletal muscle fibers is populated by a variety of mononuclear cell types. Upon acute or chronic insult, these cell populations become activated and initiate finely-orchestrated crosstalk that promotes myofiber repair and regeneration. Mass cytometry is a powerful and highly multiplexed technique for profiling single-cells. Herein, it was used to dissect the dynamics of cell populations in the skeletal muscle in physiological and pathological conditions. Here, we characterized an antibody panel that could be used to identify most of the cell populations in the muscle interstitial space. By exploiting the mass cytometry resolution, we provided a comprehensive picture of the dynamics of the major cell populations that sensed and responded to acute damage in wild type mice and in a mouse model of Duchenne muscular dystrophy. In addition, we revealed the intrinsic heterogeneity of many of these cell populations.

665 Spatial single-cell analysis of colorectal cancer tumour using multiplexed imaging mass cytometry

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A702-A702
Author(s):  
Minh Tran ◽  
Andrew Su ◽  
HoJoon Lee ◽  
Richard Cruz ◽  
Lance Pflieger ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Single Cell ◽  
Cancer Cells ◽  
Tissue Section ◽  
Single Cells ◽  
Cell Types ◽  
Mass Cytometry ◽  
Tissue Sections ◽  
Tissue Region ◽  
Hyperion Data

BackgroundCancer research experiments often require the dissociation of cells from their native tissue before molecular profiling, leading to the loss of spatial tissue context. The cancer genomics research has shifted from mostly profiling tumour DNA mutations towards the current frontier of investigating individual genes and gene products in single cells and their immediate microenvironments. Information at this level with the spatial context enables us to link cancer–causing mutations and environmental factors to outcomes in cell signalling, responses and survival that will lead to solutions for diagnosing, predicting progression and treating cancers in different individuals. In this project we aim to capture tissue morphology, cancer cell types, multi-parameter protein contents of single cells in within morphologically intact tissue sections of colorectal tumours from 52 patients.MethodsUsing Hyperion Imaging Mass Cytometry (IMC), we simultaneously profiled 16 protein markers for each tissue section, capturing molecular signatures of tissue architecture, cancer cells, and immune cells. IMC uses laser beam to accurately ablate every 1µm2 of tissue region, generating data at subcellular resolution for FFPE tissue sections on a glass microscopy slide. We selected 2–8 regions of interest (ROI), each containing approximately 2098 cells. The ROI sizes range from 141µm x 500µm to 1121µm x 1309µm. We developed an analysis pipeline to process raw Hyperion imaging data (IMCtools), define cellular masks with information about nuclei, membrane, cytoplasm (using CellProfiler and Ilastick), and analyses cellular communities (HistoCAT). We also generated whole exome sequencing data and histopathological mages from sections of the same tissue blocks.ResultsBy measuring 16 multiplexed proteins, for each tissue region we were able to identify up to seven cell types and preserved their spatial location within the tissue (figure 1A). Through the spatial map of the cell types to the tissue, we showed the heterogeneity of the tumour microenvironment, such as the infiltration of macrophages and B-cells to the cancer regions (figure 1A). We found cancer cells consistently marked as positive for p53 and Ki67 proteins. Moreover, we could measure the level of p53 in every individual cell within each tissue section (figure 1B). The quantitative measurement of p53 by imaging mass cytometry was correlated with the result from traditional genomic sequencing of p53 mutations and with the histopathological annotation.Abstract 665 Figure 1Characterizing the complexity of colorectal cancer. A) Cell types within a region of interest, defined by 16 markers. Cancer cells are consistent to the. B) Quantifying p53 expression from Hyperion dataConclusionsApplying the Hyperion technology, we could acquire rich information from each of the precious cancer samples. The spatial data at single-cell resolution enabled us to assess the heterogeneity of tumour tissue by defining cell types, immune infiltration, and cancer-immune cell interaction within an undissociated tissue section. Future analysis and application of Hyperion data would allow us to find better predictors for colorectal cancer tissue with more accurate diagnosis and prognosis.Ethics ApprovalThis study was approved by the Institutional Review Board (#1050191) at Intermountain Healthcare (Salt Lake City, UT USA)

scholarly journals DGCyTOF:  deep learning with graphic cluster visualization to predict cell types of single cell mass cytometry data

2021 ◽  
Author(s):  
Lijun Cheng ◽  
Pratik Karkhanis ◽  
Birkan Gokbag ◽  
Lang Li
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
3D Visualization ◽  
Single Cells ◽  
Cell Mass ◽  
Cell Types ◽  
Learning System ◽  
Cell Populations ◽  
Calibration System ◽  
Mass Cytometry

Background :  Single-cell mass cytometry, also known as cytometry by time of flight (CyTOF) is a powerful high-throughput technology that allows analysis of up to 50 protein markers per cell for the quantification and classification of single cells. Traditional manual gating utilized to identify new cell populations has been inadequate, inefficient, unreliable, and difficult to use, and no algorithms to identify both calibration and new cell populations has been well established. Methods :   A deep learning with graphic cluster (DGCyTOF) visualization is developed as a new integrated embedding visualization approach in identifying canonical and new cell types. The DGCyTOF combines deep-learning classification and hierarchical stable-clustering methods to sequentially build a tri-layer construct for known cell types and the identification of new cell types. First, deep classification learning is constructed to distinguish calibration cell populations from all cells by softmax classification assignment under a probability threshold, and graph embedding clustering is then used to identify new cell populations sequentially. In the middle of two-layer, cell labels are automatically adjusted between new and unknown cell populations via a feedback loop using an iteration calibration system to reduce the rate of error in the identification of cell types, and a 3-dimensional (3D) visualization platform is finally developed to display the cell clusters with all cell-population types annotated. Results : Utilizing two benchmark CyTOF databases comprising up to 43 million cells, we compared accuracy and speed in the identification of cell types among DGCyTOF, DeepCyTOF, and other technologies including dimension reduction with clustering, including Principal Component Analysis ( PCA ) , Factor Analysis ( FA ), Independent Component Analysis ( ICA ), Isometric Feature Mapping ( Isomap ), t-distributed Stochastic Neighbor Embedding ( t-SNE ), and Uniform Manifold Approximation and Projection ( UMAP ) with k -means clustering and Gaussian mixture clustering. We observed the DGCyTOF represents a robust complete learning system with high accuracy, speed and visualization by eight measurement criteria. The DGCyTOF displayed F-scores of 0.9921 for CyTOF1 and 0.9992 for CyTOF2 datasets, whereas those scores were only 0.507 and 0.529 for the t-SNE + k-means ; 0.565 and 0.59, for UMAP + k-means . Comparison of DGCyTOF with t-SNE and UMAP visualization in accuracy demonstrated its approximately 35% superiority in predicting cell types. In addition, observation of cell-population distribution was more intuitive in the 3D visualization in DGCyTOF than t-SNE and UMAP visualization. Conclusions :  The DGCyTOF model can automatically assign known labels to single cells with high accuracy using deep-learning classification assembling with traditional graph-clustering and dimension-reduction strategies. Guided by a calibration system, the model seeks optimal accuracy balance among calibration cell populations and unknown cell types, yielding a complete and robust learning system that is highly accurate in the identification of cell populations compared to results using other methods in the analysis of single-cell CyTOF data. Application of the DGCyTOF method to identify cell populations could be extended to the analysis of single-cell RNASeq data and other omics data.

scholarly journals Heritability Estimation and Differential Analysis with Generalized Linear Mixed Models in Genomic Sequencing Studies

2018 ◽  
Author(s):  
Shiquan Sun ◽  
Jiaqiang Zhu ◽  
Sahar Mozaffari ◽  
Carole Ober ◽  
Mengjie Chen ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Count Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Supplementary Information ◽  
Genomic Sequencing ◽  
Differential Analysis ◽  
Link Type ◽  
Heritability Estimation ◽  
Sequencing Studies

ABSTRACTMotivationGenomic sequencing studies, including RNA sequencing and bisulfite sequencing studies, are becoming increasingly common and increasingly large. Large genomic sequencing studies open doors for accurate molecular trait heritability estimation and powerful differential analysis. Heritability estimation and differential analysis in sequencing studies requires the development of statistical methods that can properly account for the count nature of the sequencing data and that are computationally efficient for large data sets.ResultsHere, we develop such a method, PQLseq (Penalized Quasi-Likelihood for sequencing count data), to enable effective and efficient heritability estimation and differential analysis using the generalized linear mixed model framework. With extensive simulations and comparisons to previous methods, we show that PQLseq is the only method currently available that can produce unbiased heritability estimates for sequencing count data. In addition, we show that PQLseq is well suited for differential analysis in large sequencing studies, providing calibrated type I error control and more power compared to the standard linear mixed model methods. Finally, we apply PQLseq to perform gene expression heritability estimation and differential expression analysis in a large RNA sequencing study in the Hutterites.Availability and implementationPQLseq is implemented as an R package with source code freely available at www.xzlab.org/software.html and https://cran.r-project.org/web/packages/PQLseq/index.html.ContactXZ ([email protected])Supplementary informationSupplementary data are available online.

Analyzing discontinuities in longitudinal count data: A multilevel generalized linear mixed model.

2020 ◽  
Author(s):  
James L. Peugh ◽  
Sarah J. Beal ◽  
Meghan E. McGrady ◽  
Michael D. Toland ◽  
Constance Mara
Keyword(s):  
Count Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Longitudinal Count Data

Predicting bycatch hotspots based on suitable habitat derived from fishery-independent data

2020 ◽  
Vol 641 ◽  
pp. 159-175
Author(s):  
J Runnebaum ◽  
KR Tanaka ◽  
L Guan ◽  
J Cao ◽  
L O’Brien ◽  
...  
Keyword(s):  
Habitat Suitability ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Gulf Of Maine ◽  
Homarus Americanus ◽  
Management Tool ◽  
American Lobster ◽  
Suitable Habitat ◽  
Climate Conditions ◽  
Suitability Index

Bycatch remains a global problem in managing sustainable fisheries. A critical aspect of management is understanding the timing and spatial extent of bycatch. Fisheries management often relies on observed bycatch data, which are not always available due to a lack of reporting or observer coverage. Alternatively, analyzing the overlap in suitable habitat for the target and non-target species can provide a spatial management tool to understand where bycatch interactions are likely to occur. Potential bycatch hotspots based on suitable habitat were predicted for cusk Brosme brosme incidentally caught in the Gulf of Maine American lobster Homarus americanus fishery. Data from multiple fisheries-independent surveys were combined in a delta-generalized linear mixed model to generate spatially explicit density estimates for use in an independent habitat suitability index. The habitat suitability indices for American lobster and cusk were then compared to predict potential bycatch hotspot locations. Suitable habitat for American lobster has increased between 1980 and 2013 while suitable habitat for cusk decreased throughout most of the Gulf of Maine, except for Georges Basin and the Great South Channel. The proportion of overlap in suitable habitat varied interannually but decreased slightly in the spring and remained relatively stable in the fall over the time series. As Gulf of Maine temperatures continue to increase, the interactions between American lobster and cusk are predicted to decline as cusk habitat continues to constrict. This framework can contribute to fisheries managers’ understanding of changes in habitat overlap as climate conditions continue to change and alter where bycatch interactions could occur.

Long-term seizure outcomes after pediatric temporal lobectomy: does brain MRI lesion matter?

2019 ◽  
Vol 24 (2) ◽  
pp. 200-208
Author(s):  
Ravindra Arya ◽  
Francesco T. Mangano ◽  
Paul S. Horn ◽  
Sabrina K. Kaul ◽  
Serena K. Kaul ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Hippocampal Sclerosis ◽  
Brain Mri ◽  
Temporal Lobectomy ◽  
Seizure Freedom ◽  
Seizure Onset

OBJECTIVEThere is emerging data that adults with temporal lobe epilepsy (TLE) without a discrete lesion on brain MRI have surgical outcomes comparable to those with hippocampal sclerosis (HS). However, pediatric TLE is different from its adult counterpart. In this study, the authors investigated if the presence of a potentially epileptogenic lesion on presurgical brain MRI influences the long-term seizure outcomes after pediatric temporal lobectomy.METHODSChildren who underwent temporal lobectomy between 2007 and 2015 and had at least 1 year of seizure outcomes data were identified. These were classified into lesional and MRI-negative groups based on whether an epilepsy-protocol brain MRI showed a lesion sufficiently specific to guide surgical decisions. These patients were also categorized into pure TLE and temporal plus epilepsies based on the neurophysiological localization of the seizure-onset zone. Seizure outcomes at each follow-up visit were incorporated into a repeated-measures generalized linear mixed model (GLMM) with MRI status as a grouping variable. Clinical variables were incorporated into GLMM as covariates.RESULTSOne hundred nine patients (44 females) were included, aged 5 to 21 years, and were classified as lesional (73%), MRI negative (27%), pure TLE (56%), and temporal plus (44%). After a mean follow-up of 3.2 years (range 1.2–8.8 years), 66% of the patients were seizure free for ≥ 1 year at last follow-up. GLMM analysis revealed that lesional patients were more likely to be seizure free over the long term compared to MRI-negative patients for the overall cohort (OR 2.58, p < 0.0001) and for temporal plus epilepsies (OR 1.85, p = 0.0052). The effect of MRI lesion was not significant for pure TLE (OR 2.64, p = 0.0635). Concordance of ictal electroencephalography (OR 3.46, p < 0.0001), magnetoencephalography (OR 4.26, p < 0.0001), and later age of seizure onset (OR 1.05, p = 0.0091) were associated with a higher likelihood of seizure freedom. The most common histological findings included cortical dysplasia types 1B and 2A, HS (40% with dual pathology), and tuberous sclerosis.CONCLUSIONSA lesion on presurgical brain MRI is an important determinant of long-term seizure freedom after pediatric temporal lobectomy. Pediatric TLE is heterogeneous regarding etiologies and organization of seizure-onset zones with many patients qualifying for temporal plus nosology. The presence of an MRI lesion determined seizure outcomes in patients with temporal plus epilepsies. However, pure TLE had comparable surgical seizure outcomes for lesional and MRI-negative groups.

Sign in / Sign up

Export Citation Format

Share Document