Weekly Reading List: June, 23 2025

The reported dismissal of high-ranking CBER officials Nicole Verdun and Rachael Anatol resurfaced lingering concerns about how gene therapies will be regulated under new FDA leadership.

The FDA has stopped new | The FDA has stopped new clinical trials that export American citizens’ living cells from the U.S. to “China and other hostile countries for genetic engineering and subsequent infusion” back into American patients.

Density Functional Theory (DFT) is the most widely used electronic structure method for predicting the properties of molecules and materials. Although DFT is, in principle, an exact reformulation of the Schrödinger equation, practical applications rely on approximations to the unknown exchange-correlation (XC) functional. Most existing XC functionals are constructed using a limited set of increasingly complex, hand-crafted features that improve accuracy at the expense of computational efficiency. Yet, no current approximation achieves the accuracy and generality for predictive modeling of laboratory experiments at chemical accuracy -- typically defined as errors below 1 kcal/mol. In this work, we present Skala, a modern deep learning-based XC functional that bypasses expensive hand-designed features by learning representations directly from data. Skala achieves chemical accuracy for atomization energies of small molecules while retaining the computational efficiency typical of semi-local DFT. This performance is enabled by training on an unprecedented volume of high-accuracy reference data generated using computationally intensive wavefunction-based methods. Notably, Skala systematically improves with additional training data covering diverse chemistry. By incorporating a modest amount of additional high-accuracy data tailored to chemistry beyond atomization energies, Skala achieves accuracy competitive with the best-performing hybrid functionals across general main group chemistry, at the cost of semi-local DFT. As the training dataset continues to expand, Skala is poised to further enhance the predictive power of first-principles simulations.

Open letter in <i>Science</i> says federal policy decisions ‘threaten the strength of the US research enterprise’

Every spring, Bogong moths use the starry night sky as a compass to navigate up to 1,000 km towards their alpine migratory goal.

Sarepta halts Elevidys shipments for non-ambulatory DMD patients after a second patient death in three months post gene therapy treatment.

House panel rejects cuts to agricultural research, and Senators express doubts about cuts to NIH and forest research

The hearts started to beat in the pig–human hybrids, which survived for 21 days.

The federal judge said the NIH violated federal law by arbitrarily canceling more than $1 billion in research grants because of their perceived connection to DEI initiatives.

This Review discusses current 2D and 3D microscopy methods for imaging three-dimensional cell cultures and emerging strategies to address key challenges.

Single-cell RNA sequencing has revolutionized our understanding of cellular diversity but remains constrained by scalability, high costs, and the destruction of cells during analysis. To overcome these challenges, we developed STAMP (single-cell transcriptomics analysis and multimodal profiling), a highly scalable approach for the profiling of single cells. By leveraging transcriptomics and proteomics imaging platforms, STAMP eliminates sequencing costs, enabling cost-efficient single-cell genomics of millions of cells. Immobilizing (stamping) cells in suspension onto imaging slides, STAMP supports multimodal (RNA, protein, and H&E) profiling, while retaining cellular structure and morphology.

The Academy report, funded by a grant from the Gordon and Betty Moore Foundation, examines the origins and trajectory of early microbial life to inform and inspire future research.

Scientific Reports - Evaluation of automatic cell free DNA extraction metrics using different blood collection tubes

The AI unicorn’s Perturb-seq atlas is publicly available and detects dose-dependent genetic effects to enhance predictive power for drug discovery.

Studies of the regenerative powers of worms, zebrafish and lizards suggest ways to improve recovery in people.

Half a century after its cinematic release, Jaws is still shaping how we view — and protect — the ocean’s top predators.

Last week I double-dipped on conferences, going from London Calling to European Society for Human Genetics (ESHG) in Milan. I have a raft o…

Newborns will have whole genome sequencing to enable personalised healthcare that predicts and prevents illness

We present a computational approach to the design of high-efficiency enzymes with catalytic parameters comparable to natural enzymes, enabling programming of stable, high-efficiency, new-to-nature Kemp elimination enzymes through minimal experimental effort.

It is squarely unethical to treat healthy pregnant women differently than other groups that are similarly at elevated risk of serious Covid illness, writes one bioethicist.

Background Transcriptome sequencing (RNA-seq) is a powerful technology for gene expression profiling. Selection of optimal parameters for cDNA library generation is crucial for acquisition of high-quality data. In this study, we investigate the impact of the amount of RNA and the number of PCR cycles used for sample amplification on the rate of PCR duplication and, in consequence, on the RNA-seq data quality. Results For broader applicability, we sequenced the data on four short-read sequencing platforms: Illumina NovaSeq 6000, Illumina NovaSeq X, Element Biosciences AVITI, and Singular Genomics G4. The native Illumina libraries were converted for sequencing on AVITI and G4 to assess the effect of library conversion, containing additional PCR cycles. We find that the rate of PCR duplicates depends on the combined effect of RNA input material and the number of PCR cycles used for amplification. For input amounts lower than 125 ng, 34–96% of reads were discarded via deduplication with the percentage increasing with lower input amount and decreasing with increasing PCR cycles. The reduced read diversity for low input amounts leads to fewer genes detected and increased noise in expression counts. Conclusions Data generated with each of the four sequencing platforms presents similar associations between starting material amount and the number of PCR cycles on PCR duplicates, a similar number of detected genes, and comparable gene expression profiles.

SAVANA is a tool to detect somatic structural variants and copy number aberrations using long-read sequencing data, offering high sensitivity, specificity and compatibility with or without germline controls.

Will birthdays go the way of the Betamax and Blackberry? Our culture is always eager to move away from old things toward new things and these days if you want to know how old you are, the number of candles on your cake is just one clue — and maybe not even the best clue. Epigenetic clocks measure what’s happening inside you on a cellular level and they might say you’re aging faster (or slower) than you thought based on changes to your DNA.

The Trump administration’s gutting of global aid is threatening to collapse a network of laboratories responsible for global measles and rubella surveillance

As stem cell research gains momentum, researchers are looking at ways to use the therapy to treat hearing loss.