scholarly journals FISHFactor: A Probabilistic Factor Model for Spatial Transcriptomics Data with Subcellular Resolution

2021 ◽  
Author(s):  
Florin Cornelius Walter ◽  
Oliver Stegle ◽  
Britta Velten
Keyword(s):  
Factor Analysis ◽  
Single Molecule ◽  
Factor Model ◽  
Expression Patterns ◽  
Real Data ◽  
Gene Clusters ◽  
Transcriptomics Data ◽  
Subcellular Resolution ◽  
Nih 3T3

Factor analysis is a widely-used method for dimensionality reduction of high-throughput datasets in molecular biology and has recently been adapted to spatial transcriptomics data. However, existing methods assume (count) matrices as input and are therefore not directly applicable to single-molecule resolved data, which increasingly arise for example from multiplexed fluorescence in-situ hybridization or in-situ sequencing experiments. To address this, we here propose FISHFactor, a probabilistic model that combines the benefits of spatial, non-negative factor analysis with a Poisson point process likelihood to explicitly model and account for the nature of single-molecule resolved data. FISHFactor furthermore leverages principles of multi-modal factor analysis to enable dissecting the transcriptional heterogeneity between multiple groups of samples, such as different cells. Using simulated and real data, we show that our approach leads to improved estimates of the true spatial transcriptome landscape compared to existing methods that rely on aggregating information by spatial binning. Applied to a set of NIH/3T3 cells, FISHFactor identifies major subcellular expression patterns and accurately recovers known spatial gene clusters.

scholarly journals SSAM-lite: a light-weight web app for rapid analysis of spatially resolved transcriptomics data

2021 ◽  
Author(s):  
Sebastian Tiesmeyer ◽  
Shashwat Sahay ◽  
Niklas Müller-Bötticher ◽  
Roland Eils ◽  
Sebastian D. Mackowiak ◽  
...  
Keyword(s):  
Programming Languages ◽  
Single Molecule ◽  
Free Cell ◽  
Cell Segmentation ◽  
Web Browser ◽  
Spatially Resolved ◽  
Transcriptomics Data ◽  
Specialized Hardware ◽  
Computational Resources

The combination of a cell's transcriptional profile and location defines its function in a spatial context. Spatially resolved transcriptomics (SRT) has emerged as the assay of choice for characterizing cells in situ. SRT methods can resolve gene expression up to single-molecule resolution. A particular computational problem with single-molecule SRT methods is the correct aggregation of mRNA molecules into cells. Traditionally, aggregating mRNA molecules into cell-based features begins with the identification of cells via segmentation of the nucleus or the cell membrane. However, recently a number of cell-segmentation-free approaches have emerged. While these methods have been demonstrated to be more performant than segmentation-based approaches, they are still not easily accessible since they require specialized knowledge of programming languages and access to large computational resources. Here we present SSAM-lite, a tool that provides an easy-to-use graphical interface to perform rapid and segmentation-free cell-typing of SRT data in a web browser. SSAM-lite runs locally and does not require computational experts or specialized hardware. Analysis of a tissue slice of the mouse somatosensory cortex took less than a minute on a laptop with modest hardware. Parameters can interactively be optimized on small portions of the data before the entire tissue image is analyzed. A server version of SSAM-lite can be run completely offline using local infrastructure. Overall, SSAM-lite is portable, lightweight, and easy to use, thus enabling a broad audience to investigate and analyze single-molecule SRT data.

Factorial Structure of the Therapist Adherence Measure-Revised (TAM-R) Within Multisystemic Therapy

2020 ◽  
Vol 36 (2) ◽  
pp. 427-431
Author(s):  
Aurelie M. C. Lange ◽  
Marc J. M. H. Delsing ◽  
Ron H. J. Scholte ◽  
Rachel E. A. van der Rijken
Keyword(s):  
Factor Analysis ◽  
Factor Structure ◽  
Factor Model ◽  
Multisystemic Therapy ◽  
Model Fit ◽  
Validity And Reliability ◽  
Adherence Measure ◽  
Therapist Adherence ◽  
Confirmatory Factor ◽  
The Us

Abstract. The Therapist Adherence Measure (TAM-R) is a central assessment within the quality-assurance system of Multisystemic Therapy (MST). Studies into the validity and reliability of the TAM in the US have found varying numbers of latent factors. The current study aimed to reexamine its factor structure using two independent samples of families participating in MST in the Netherlands. The factor structure was explored using an Exploratory Factor Analysis (EFA) in Sample 1 ( N = 580). This resulted in a two-factor solution. The factors were labeled “therapist adherence” and “client–therapist alliance.” Four cross-loading items were dropped. Reliability of the resulting factors was good. This two-factor model showed good model fit in a subsequent Confirmatory Factor Analysis (CFA) in Sample 2 ( N = 723). The current finding of an alliance component corroborates previous studies and fits with the focus of the MST treatment model on creating engagement.

scholarly journals Transcriptome analysis and molecular mechanism of linseed (Linum usitatissimum L.) drought tolerance under repeated drought using single-molecule long-read sequencing

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Wei Wang ◽  
Lei Wang ◽  
Ling Wang ◽  
Meilian Tan ◽  
Collins O. Ogutu ◽  
...  
Keyword(s):  
Dna Repair ◽  
Drought Tolerance ◽  
Single Molecule ◽  
Linum Usitatissimum ◽  
Expression Patterns ◽  
Transcriptional Response ◽  
Enrichment Analysis ◽  
Resistant Variety ◽  
Proline Biosynthesis ◽  
Interacting Protein

Abstract Background Oil flax (linseed, Linum usitatissimum L.) is one of the most important oil crops., However, the increases in drought resulting from climate change have dramatically reduces linseed yield and quality, but very little is known about how linseed coordinates the expression of drought resistance gene in response to different level of drought stress (DS) on the genome-wide level. Results To explore the linseed transcriptional response of DS and repeated drought (RD) stress, we determined the drought tolerance of different linseed varieties. Then we performed full-length transcriptome sequencing of drought-resistant variety (Z141) and drought-sensitive variety (NY-17) under DS and RD stress at the seedling stage using single-molecule real-time sequencing and RNA-sequencing. Gene Ontology (GO) and reduce and visualize GO (REVIGO) enrichment analysis showed that upregulated genes of Z141 were enriched in more functional pathways related to plant drought tolerance than those of NY-17 were under DS. In addition, 4436 linseed transcription factors were identified, and 1190 were responsive to stress treatments. Moreover, protein-protein interaction (PPI) network analysis showed that the proline biosynthesis pathway interacts with stress response genes through RAD50 (DNA repair protein 50) interacting protein 1 (RIN-1). Finally, proline biosynthesis and DNA repair structural gene expression patterns were verified by RT- PCR. Conclusions The drought tolerance of Z141 may be related to its upregulation of drought tolerance genes under DS. Proline may play an important role in linseed drought tolerance by maintaining cell osmotic and protecting DNA from ROS damage. In summary, this study provides a new perspective to understand the drought adaptability of linseed.

An investigation into the factor structure of the Cognitive Therapy Scale – Revised (CTS-R) in a CBT training sample

2021 ◽  
pp. 1-11
Author(s):  
Sarah Beale ◽  
Silia Vitoratou ◽  
Sheena Liness
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Cognitive Therapy ◽  
Exploratory Factor Analysis ◽  
Factor Structure ◽  
Factor Model ◽  
Single Factor ◽  
Behaviour Therapy ◽  
Cognitive Behaviour ◽  
Confirmatory Factor

Abstract Background: Effective monitoring of cognitive behaviour therapy (CBT) competence depends on psychometrically robust assessment methods. While the UK Cognitive Therapy Scale – Revised (CTS-R; Blackburn et al., 2001) has become a widely used competence measure in CBT training, practice and research, its underlying factor structure has never been investigated. Aims: This study aimed to present the first investigation into the factor structure of the CTS-R based on a large sample of postgraduate CBT trainee recordings. Method: Trainees (n = 382) provided 746 mid-treatment audio recordings for depression (n = 373) and anxiety (n = 373) cases scored on the CTS-R by expert markers. Tapes were split into two equal samples counterbalanced by diagnosis and with one tape per trainee. Exploratory factor analysis was conducted. The suggested factor structure and a widely used theoretical two-factor model were tested with confirmatory factor analysis. Measurement invariance was assessed by diagnostic group (depression versus anxiety). Results: Exploratory factor analysis suggested a single-factor solution (98.68% explained variance), which was supported by confirmatory factor analysis. All 12 CTS-R items were found to contribute to this single factor. The univariate model demonstrated full metric invariance and partial scalar invariance by diagnosis, with one item (item 10 – Conceptual Integration) demonstrating scalar non-invariance. Conclusions: Findings indicate that the CTS-R is a robust homogenous measure and do not support division into the widely used theoretical generic versus CBT-specific competency subscales. Investigation into the CTS-R factor structure in other populations is warranted.

scholarly journals sepal: identifying transcript profiles with spatial patterns by diffusion-based modeling

2021 ◽  
Author(s):  
Alma Andersson ◽  
Joakim Lundeberg
Keyword(s):  
Spatial Patterns ◽  
Expression Profiles ◽  
Synthetic Data ◽  
Real Data ◽  
Cell Types ◽  
Statistical Hypothesis ◽  
Supplementary Information ◽  
Statistical Hypothesis Testing ◽  
Transcriptomics Data ◽  
Transcript Profiles

Abstract Motivation Collection of spatial signals in large numbers has become a routine task in multiple omics-fields, but parsing of these rich datasets still pose certain challenges. In whole or near-full transcriptome spatial techniques, spurious expression profiles are intermixed with those exhibiting an organized structure. To distinguish profiles with spatial patterns from the background noise, a metric that enables quantification of spatial structure is desirable. Current methods designed for similar purposes tend to be built around a framework of statistical hypothesis testing, hence we were compelled to explore a fundamentally different strategy. Results We propose an unexplored approach to analyze spatial transcriptomics data, simulating diffusion of individual transcripts to extract genes with spatial patterns. The method performed as expected when presented with synthetic data. When applied to real data, it identified genes with distinct spatial profiles, involved in key biological processes or characteristic for certain cell types. Compared to existing methods, ours seemed to be less informed by the genes’ expression levels and showed better time performance when run with multiple cores. Availabilityand implementation Open-source Python package with a command line interface (CLI), freely available at https://github.com/almaan/sepal under an MIT licence. A mirror of the GitHub repository can be found at Zenodo, doi: 10.5281/zenodo.4573237. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Genomic Characteristics and Comparative Genomics Analysis of Two Chinese Corynespora cassiicola Strains Causing Corynespora Leaf Fall (CLF) Disease

2021 ◽  
Vol 7 (6) ◽  
pp. 485
Author(s):  
Boxun Li ◽  
Yang Yang ◽  
Jimiao Cai ◽  
Xianbao Liu ◽  
Tao Shi ◽  
...  
Keyword(s):  
Comparative Genomics ◽  
Single Molecule ◽  
Rubber Tree ◽  
Gene Families ◽  
Gene Clusters ◽  
The Philippines ◽  
Tree Plantations ◽  
Comparative Genomic ◽  
Corynespora Cassiicola ◽  
Leaf Fall

Rubber tree Corynespora leaf fall (CLF) disease, caused by the fungus Corynespora cassiicola, is one of the most damaging diseases in rubber tree plantations in Asia and Africa, and this disease also threatens rubber nurseries and young rubber plantations in China. C. cassiicola isolates display high genetic diversity, and virulence profiles vary significantly depending on cultivar. Although one phytotoxin (cassicolin) has been identified, it cannot fully explain the diversity in pathogenicity between C. cassiicola species, and some virulent C. cassiicola strains do not contain the cassiicolin gene. In the present study, we report high-quality gapless genome sequences, obtained using short-read sequencing and single-molecule long-read sequencing, of two Chinese C. cassiicola virulent strains. Comparative genomics of gene families in these two stains and a virulent CPP strain from the Philippines showed that all three strains experienced different selective pressures, and metabolism-related gene families vary between the strains. Secreted protein analysis indicated that the quantities of secreted cell wall-degrading enzymes were correlated with pathogenesis, and the most aggressive CCP strain (cassiicolin toxin type 1) encoded 27.34% and 39.74% more secreted carbohydrate-active enzymes (CAZymes) than Chinese strains YN49 and CC01, respectively, both of which can only infect rubber tree saplings. The results of antiSMASH analysis showed that all three strains encode ~60 secondary metabolite biosynthesis gene clusters (SM BGCs). Phylogenomic and domain structure analyses of core synthesis genes, together with synteny analysis of polyketide synthase (PKS) and non-ribosomal peptide synthetase (NRPS) gene clusters, revealed diversity in the distribution of SM BGCs between strains, as well as SM polymorphisms, which may play an important role in pathogenic progress. The results expand our understanding of the C. cassiicola genome. Further comparative genomic analysis indicates that secreted CAZymes and SMs may influence pathogenicity in rubber tree plantations. The findings facilitate future exploration of the molecular pathogenic mechanism of C. cassiicola.

scholarly journals Reconstruction of Microbial Haplotypes by Integration of Statistical and Physical Linkage in Scaffolding

2021 ◽  
Author(s):  
Chen Cao ◽  
Jingni He ◽  
Lauren Mak ◽  
Deshan Perera ◽  
Devin Kwok ◽  
...  
Keyword(s):  
Single Molecule ◽  
Human Genetics ◽  
Real Data ◽  
Sequencing Technologies ◽  
Bacterial Genomics ◽  
Physical Linkage ◽  
Pooled Sequencing ◽  
Computational Reconstruction ◽  
Host Genetic ◽  
Host Evolution

Abstract DNA sequencing technologies provide unprecedented opportunities to analyze within-host evolution of microorganism populations. Often, within-host populations are analyzed via pooled sequencing of the population, which contains multiple individuals or “haplotypes.” However, current next-generation sequencing instruments, in conjunction with single-molecule barcoded linked-reads, cannot distinguish long haplotypes directly. Computational reconstruction of haplotypes from pooled sequencing has been attempted in virology, bacterial genomics, metagenomics, and human genetics, using algorithms based on either cross-host genetic sharing or within-host genomic reads. Here, we describe PoolHapX, a flexible computational approach that integrates information from both genetic sharing and genomic sequencing. We demonstrated that PoolHapX outperforms state-of-the-art tools tailored to specific organismal systems, and is robust to within-host evolution. Importantly, together with barcoded linked-reads, PoolHapX can infer whole-chromosome-scale haplotypes from 50 pools each containing 12 different haplotypes. By analyzing real data, we uncovered dynamic variations in the evolutionary processes of within-patient HIV populations previously unobserved in single position-based analysis.

scholarly journals Medical artificial intelligence readiness scale for medical students (MAIRS-MS) – development, validity and reliability study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ozan Karaca ◽  
S. Ayhan Çalışkan ◽  
Kadir Demir
Keyword(s):  
Artificial Intelligence ◽  
Factor Analysis ◽  
Medical Students ◽  
Data Science ◽  
Factor Model ◽  
Health Science ◽  
Measurement Tool ◽  
Validity And Reliability ◽  
Mathematical Concepts ◽  
Evaluation And Monitoring

Abstract Background It is unlikely that applications of artificial intelligence (AI) will completely replace physicians. However, it is very likely that AI applications will acquire many of their roles and generate new tasks in medical care. To be ready for new roles and tasks, medical students and physicians will need to understand the fundamentals of AI and data science, mathematical concepts, and related ethical and medico-legal issues in addition with the standard medical principles. Nevertheless, there is no valid and reliable instrument available in the literature to measure medical AI readiness. In this study, we have described the development of a valid and reliable psychometric measurement tool for the assessment of the perceived readiness of medical students on AI technologies and its applications in medicine. Methods To define medical students’ required competencies on AI, a diverse set of experts’ opinions were obtained by a qualitative method and were used as a theoretical framework, while creating the item pool of the scale. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were applied. Results A total of 568 medical students during the EFA phase and 329 medical students during the CFA phase, enrolled in two different public universities in Turkey participated in this study. The initial 27-items finalized with a 22-items scale in a four-factor structure (cognition, ability, vision, and ethics), which explains 50.9% cumulative variance that resulted from the EFA. Cronbach’s alpha reliability coefficient was 0.87. CFA indicated appropriate fit of the four-factor model (χ2/df = 3.81, RMSEA = 0.094, SRMR = 0.057, CFI = 0.938, and NNFI (TLI) = 0.928). These values showed that the four-factor model has construct validity. Conclusions The newly developed Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) was found to be valid and reliable tool for evaluation and monitoring of perceived readiness levels of medical students on AI technologies and applications. Medical schools may follow ‘a physician training perspective that is compatible with AI in medicine’ to their curricula by using MAIRS-MS. This scale could be benefitted by medical and health science education institutions as a valuable curriculum development tool with its learner needs assessment and participants’ end-course perceived readiness opportunities.

scholarly journals RNA In Situ Hybridization for Detecting Gene Expression Patterns in the Abdomens and Wings of Drosophila Species

2021 ◽  
Vol 4 (1) ◽  
pp. 20
Author(s):  
Mujeeb Shittu ◽  
Tessa Steenwinkel ◽  
William Dion ◽  
Nathan Ostlund ◽  
Komal Raja ◽  
...  
Keyword(s):  
Gene Expression ◽  
In Situ Hybridization ◽  
Expression Patterns ◽  
Drosophila Species ◽  
Gene Expression Patterns ◽  
Probe Design ◽  
Tissue Preparation ◽  
Design And Synthesis ◽  
Rna In Situ Hybridization

RNA in situ hybridization (ISH) is used to visualize spatio-temporal gene expression patterns with broad applications in biology and biomedicine. Here we provide a protocol for mRNA ISH in developing pupal wings and abdomens for model and non-model Drosophila species. We describe best practices in pupal staging, tissue preparation, probe design and synthesis, imaging of gene expression patterns, and image-editing techniques. This protocol has been successfully used to investigate the roles of genes underlying the evolution of novel color patterns in non-model Drosophila species.

scholarly journals Spatial expression pattern of serine proteases in the blood fluke Schistosoma mansoni determined by fluorescence RNA in situ hybridization

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Lenka Ulrychová ◽  
Pavel Ostašov ◽  
Marta Chanová ◽  
Michael Mareš ◽  
Martin Horn ◽  
...  
Keyword(s):  
In Situ Hybridization ◽  
Schistosoma Mansoni ◽  
Rna Sequencing ◽  
Serine Proteases ◽  
Expression Patterns ◽  
High Sensitivity ◽  
Host Parasite Interactions ◽  
Highly Sensitive ◽  
Host Parasite

Abstract Background The blood flukes of genus Schistosoma are the causative agent of schistosomiasis, a parasitic disease that infects more than 200 million people worldwide. Proteases of schistosomes are involved in critical steps of host–parasite interactions and are promising therapeutic targets. We recently identified and characterized a group of S1 family Schistosoma mansoni serine proteases, including SmSP1 to SmSP5. Expression levels of some SmSPs in S. mansoni are low, and by standard genome sequencing technologies they are marginally detectable at the method threshold levels. Here, we report their spatial gene expression patterns in adult S. mansoni by the high-sensitivity localization assay. Methodology Highly sensitive fluorescence in situ RNA hybridization (FISH) was modified and used for the localization of mRNAs encoding individual SmSP proteases (including low-expressed SmSPs) in tissues of adult worms. High sensitivity was obtained due to specifically prepared tissue and probes in combination with the employment of a signal amplification approach. The assay method was validated by detecting the expression patterns of a set of relevant reference genes including SmCB1, SmPOP, SmTSP-2, and Sm29 with localization formerly determined by other techniques. Results FISH analysis revealed interesting expression patterns of SmSPs distributed in multiple tissues of S. mansoni adults. The expression patterns of individual SmSPs were distinct but in part overlapping and were consistent with existing transcriptome sequencing data. The exception were genes with significantly low expression, which were also localized in tissues where they had not previously been detected by RNA sequencing methods. In general, SmSPs were found in various tissues including reproductive organs, parenchymal cells, esophagus, and the tegumental surface. Conclusions The FISH-based assay provided spatial information about the expression of five SmSPs in adult S. mansoni females and males. This highly sensitive method allowed visualization of low-abundantly expressed genes that are below the detection limits of standard in situ hybridization or by RNA sequencing. Thus, this technical approach turned out to be suitable for sensitive localization studies and may also be applicable for other trematodes. The results suggest that SmSPs may play roles in diverse processes of the parasite. Certain SmSPs expressed at the surface may be involved in host–parasite interactions. Graphic abstract

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