Thousandfold Expansion Microscopy

Amazing stuff!

"... In a preprint posted last month to bioRxiv, Boyden and colleagues describe “thousandfold expansion microscopy” or 1000ExM, a method that expands tissues as much as 1000x in every direction—a one billion times increase in volume.

First, they improved upon the expanding gel used to spread things apart. They also figured out how to bind target molecules to this gel, allowing them to break apart proteins and other bonded entities while keeping their pieces relative in space. Lastly, they tinkered with their technique to allow them to repeat the expansion step over and over again. ..."

From the abstract:

"Biological macromolecules, such as proteins, are made of concatenated building blocks. We hypothesized that individual protein residues could be imaged by anchoring their side chains to a swellable polymer, cleaving backbone amide bonds, and expanding residues away from each other to a degree that enables them to be visualized separately.

We introduce thousandfold expansion microscopy (1000ExM), a four-network interpenetrating hydrogel architecture that enables successive expansion from ∼18-fold to >1000-fold (one billion-fold in volume). Protein and peptide structures are maintained across these expansion factors, as verified by analyses of proteins with known structures (nanobodies, GFP) and a well-studied peptide (mCLING). Computational analysis indicates that 1000ExM resolves adjacent amino acid residues, thereby achieving sub-nanometer precision on conventional light microscopes. We anticipate that 1000ExM will find wide utility in protein visualization and identification, potentially even in intact cells and tissues."

ScienceAdviser

Thousandfold Expansion Microscopy (preprint, open access)

A small peptide with one end tagged in purple and certain amino acids in green, showing the difference in resolution between 18x expansion (top; full field on left, zoom in orange on right) and 1000x expansion.

Fig. 1 Design of a four-network interpenetrating polymer network (IPN) architecture enabling ∼1000× linear expansion via recursive ionic-in-ionic casting.

Shanghai AI Lab releases open protocol to connect scientific research agents

Good news! The dawn of automated scientific research thanks to ML & AI!

"The Shanghai Artificial Intelligence Laboratory released the Science Context Protocol, an open-source standard designed to connect AI agents, researchers, and lab equipment across institutional boundaries. SCP builds on Anthropic’s Model Context Protocol by adding structured experiment metadata, centralized hub architecture for coordinating multiple agents, intelligent workflow orchestration, and standardized drivers for lab devices.

The protocol already supports over 1,600 scientific tools spanning biology, physics, chemistry, and materials science, and enables automated workflows from experimental design through execution and validation. Researchers can deploy their own SCP infrastructure or use the lab’s hosted Intern-Discovery platform to register and share resources."

From the abstract:

"We introduce SCP: the Science Context Protocol, an open-source standard designed to accelerate discovery by enabling a global network of autonomous scientific agents.

SCP is built on two foundational pillars:

(1) Unified Resource Integration: At its core, SCP provides a universal specification for describing and invoking scientific resources, spanning software tools, models, datasets, and physical instruments. This protocol-level standardization enables AI agents and applications to discover, call, and compose capabilities seamlessly across disparate platforms and institutional boundaries.

(2) Orchestrated Experiment Lifecycle Management: SCP complements the protocol with a secure service architecture, which comprises a centralized SCP Hub and federated SCP Servers.

This architecture manages the complete experiment lifecycle (registration, planning, execution, monitoring, and archival), enforces fine-grained authentication and authorization, and orchestrates traceable, end-to-end workflows that bridge computational and physical laboratories.

Based on SCP, we have constructed a scientific discovery platform that offers researchers and agents a large-scale ecosystem of more than 1,600 tool resources. Across diverse use cases, SCP facilitates secure, large-scale collaboration between heterogeneous AI systems and human researchers while significantly reducing integration overhead and enhancing reproducibility.

By standardizing scientific context and tool orchestration at the protocol level, SCP establishes essential infrastructure for scalable, multi-institution, agent-driven science."

Data Points: Meta buys Manus for its agentic tech

SCP: Accelerating Discovery with a Global Web of Autonomous Scientific Agents (preprint, open access, 106 pages)

On Towards an AI co-scientist ushering an era of AI empowered scientists

Amazing stuff! This seems to be an interesting paper by Google and Stanford U with enormous potential!

Caveat: I did not read the paper.

"... In many fields, this [explosion of related research] presents a breadth and depth conundrum, since it is challenging to navigate the rapid growth in the rate of scientific publications while integrating insights from unfamiliar domains. ...

The AI co-scientist is a multi-agent AI system that is intended to function as a collaborative tool for scientists. Built on Gemini 2.0, AI co-scientist is designed to mirror the reasoning process underpinning the scientific method. Beyond standard literature review, summarization and “deep research” tools, the AI co-scientist system is intended to uncover new, original knowledge and to formulate demonstrably novel research hypotheses and proposals, building upon prior evidence and tailored to specific research objectives. ..."

From the abstract:

"Scientific discovery relies on scientists generating novel hypotheses that undergo rigorous experimental validation. To augment this process, we introduce an AI co-scientist, a multi-agent system built on Gemini 2.0. The AI co-scientist is intended to help uncover new, original knowledge and to formulate demonstrably novel research hypotheses and proposals, building upon prior evidence and aligned to scientist-provided research objectives and guidance.

The system's design incorporates a generate, debate, and evolve approach to hypothesis generation, inspired by the scientific method and accelerated by scaling test-time compute.

Key contributions include:

(1) a multi-agent architecture with an asynchronous task execution framework for flexible compute scaling;

(2) a tournament evolution process for self-improving hypotheses generation. Automated evaluations show continued benefits of test-time compute, improving hypothesis quality.

While general purpose, we focus development and validation in three biomedical areas: drug repurposing, novel target discovery, and explaining mechanisms of bacterial evolution and anti-microbial resistance.

For drug repurposing, the system proposes candidates with promising validation findings, including candidates for acute myeloid leukemia that show tumor inhibition in vitro at clinically applicable concentrations.

For novel target discovery, the AI co-scientist proposed new epigenetic targets for liver fibrosis, validated by anti-fibrotic activity and liver cell regeneration in human hepatic organoids.

Finally, the AI co-scientist recapitulated unpublished experimental results via a parallel in silico discovery of a novel gene transfer mechanism in bacterial evolution. These results, detailed in separate, co-timed reports, demonstrate the potential to augment biomedical and scientific discovery and usher an era of AI empowered scientists."

Accelerating scientific breakthroughs with an AI co-scientist "We introduce AI co-scientist, a multi-agent AI system built with Gemini 2.0 as a virtual scientific collaborator to help scientists generate novel hypotheses and research proposals, and to accelerate the clock speed of scientific and biomedical discoveries."

[2502.18864] Towards an AI co-scientist

Credits: Last Week in AI

AI co-scientist system overview. Specialized agents (red boxes, with unique roles and logic); scientist input and feedback (blue boxes); system information flow (dark gray arrows); inter-agent feedback (red arrows within the agent section).

On Omnigenetics: Futile?

Posted: 7/5/2018

Trigger

Recently, I read this article in the Quanta Magazine: Theory Suggests That All Genes Affect Every Complex Trait. Here is the full text of what I believe is the underlying scientific article: An Expanded View of Complex Traits: From Polygenic to Omnigenic

Omni

If you do not know much about a subject, but you want to impress, then use the prefix omni. Sounds omnipotent, but it is probably more impotent than thought.

Since environmentalism became en vogue in the 1960s and 1970s, we were to believe that everything depends on everything. From Chaos theory, we learnt that a single flap of a butterfly wing in China (or was it a tipped over rice bag) can trigger a hurricane over the Carribean Ocean (to paraphrase; an exaggeration).

Whether this kind of thinking advances science is the question.