Biodock Blog

Seeing is believing: How image-based profiling is revolutionizing the process of drug discovery

While examining molded petri dishes after a vacation in 1928, Sir Alexander Fleming noticed that the periphery of the mold had no bacterial growth. He would come to find that the mold was secreting an antibacterial agent later called penicillin. This drug discovery revolutionized medicine.

Using Biodock to do science: PhD candidate's experience

As a scientist who has collected and analyzed many images, I am well acquainted with the toil of manual analysis and the impressive potential utility Biodock has to offer. To instantiate this utility, I tried out Biodock myself.

AI-assisted image analysis

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.

Peering beyond the surface with Gradient Index lenses

How do we look beyond the surface when imaging tissue? Today's article focuses on Gradient Index (GRIN) optics and how GRIN lenses are serving as a window into the deepest areas of the brain and body

Semantic vs. Instance Segmentation: Applications for Biology

Consider a crowded room of your friends. In a snapshot of the image, semantic and instance segmentation models can be used to pick out people from the background. In the semantic model the output would be a silhouette of the whole group against the background.

Biodock's end-to-end deep AI platform is coming out of beta

The Biodock team is incredibly excited to announce that we are coming out of beta today, unveiling our end-to-end deep AI platform for biological images. Biodock makes it an afternoon&

Playing the brain's music back to itself with holographic optogenetics

Chopin’s prelude No. 4 [https://www.youtube.com/watch?v=KHGHhYZCIQI] in E minor is widely venerated for its authentic musical depiction of despair. The haunting melody from gripping

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.

One vs two-photon microscopy: Why have one when you can have two?

Need to image deeper? Ditch the one-photon microscope and learn the advantages of two photon microscopy.

$Cheating the Diffraction Limit with Expansion Microscopy$

Cheating the Diffraction Limit with Expansion Microscopy

Imagine you are behind the camera framing a picture of your friend. Where you stand, you notice that although you are capturing much of the background, your friend is far

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.

Carving out brain structure with connectomics

Over two centuries ago Lewis and Clark embarked on an expedition to map the western United States. Charting landscapes and routes in between, their adventure into this unknown territory revolutionized

Seeing clearly yet?: An overview of tissue clearing

Are you having issues imaging deep within your sample? Why not turn your specimen clear? Clearing is a great way to studying 3D structures in intact tissue for highly scalable applications.

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.

bright-field

Flavors of microscopy in biomedical research

Biomedical researchers have different microscopy tools at their disposal. This article covers the major techniques, how they function and uses.

microscopy

Microscopic resolution limbo: how low can you go?

Classically, light microscopes limits are in the micrometer range. Advances in single-molecule localization microscopy allow for nanometer resolution.

high-content screening

High-Content Screening and Imaging

You have probably heard of High-throughput screening, in which a large number of conditions are tested in parallel. The term High-Content Screening is similar, but specifically refers to a process where instead of just one endpoint, you measure many.

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.

microscopy

Image Deconvolution: How it works and how to use it for microscopy

An introduction to deconvolution for biologists: what it is, how it works, tools for biologists to do deconvolution, and more resources.

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.

Announcing Biodock's Pre-seed Round

We are incredibly excited to announce that Biodock has raised $2.1 million in pre-seed funding led by Andreessen Horowitz, with participation from TQ Ventures, Soma Capital, and leading angels like John Curtius, partner at Tiger Global, and Zach Weinberg, co-founder of Flatiron Health.

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