Medeea Popescu - Biodock

Mapping the microbiome: FISH-based approaches for microbial community visualization

Understanding what human-associated bacterial biofilms look like has major translational potential and medical impact. Here, we discuss RNA-FISH-based approaches for visualization of microbial communities.

AI-assisted image analysis

Triple Threat: AI-assisted Multiview Confocal Imaging

One group of researchers is bringing deep learning to that workhorse of bioimaging: confocal microscopy. In a recent paper, Wu et al. present a new microscopy platform that builds on confocal using AI approaches and technical innovations.

AI-assisted image analysis

From Actin to Z-stacks: The Imaging Journey

Bioimaging is a well-established and highly effective tool for interrogating biological systems. In this article, we’ll focus on the process, practice, and problems associated with setting up, running, and analyzing a fluorescence microscopy experiment.

AI-assisted image analysis

Of tumors and TMEs: new findings in pancreatic cancer spatial heterogeneity

Grünwald et al. make major progress towards untangling cancer heterogeneity with a recent paper in Cell.

AI-assisted image analysis

Putting the Pieces Together: Spatial Transcriptomics Methods and Multi-omic Integration

In this article, we'll give an overview of the different spatial sequencing methods currently available, discuss analysis approaches and challenges for spatial data, and cover methods for integrating single cell and spatial transcriptomics.

cell segmentation

How to train your model: LIVECell for AI-assisted cell segmentation

Edlund et al. created a dataset of phase-contrast images encompassing including 8 morphologically different cell types and trained a CNN-based model to evaluate the model’s cell segmentation accuracy.

Biological data analysis

FISHing for RNA: an overview of fluorescent in situ hybridization methods for RNA detection

Fluorescent in situ hybridization (FISH) is the gold standard for visualizing RNA expression and localization in cell or tissue sections, and major methodological advances in ease of use, sensitivity, and reproducibility have made it easier than ever to incorporate RNA-FISH into your next paper.

Single-cell RNA sequencing

Integrating scRNAseq and spatial sequencing drives new findings on leprosy

In a recent paper by Ma et. al. from the Modlin group at UCLA, researchers report new insights into how the immune system controls leprosy within granulomas using a powerful combination of spatial and non-spatial sequencing and dataset integration.

AI and deconvolution

More data, more problems: Perspectives on challenges in large-scale, high-complexity bioimage analysis

Advances in high-parameter imaging technologies over the past several decades created an exponential increase in the complexity of imaging datasets. In this article, we’ll cover: in situ imaging technologies, deep learning for image analysis, and unmet challenges bioimaging dataset analysis.

Biological data analysis

Visualizing and analyzing high-dimensional biological datasets in R: Focus on Luminex analysis

If you’re a scientist working in biomedical research today, chances are the datasets you’re working with are getting bigger and bigger. In this article, I’ll be describing several real-life analysis solutions involving large datasets, written for non-computational biologists.

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