Evaluating and Enhancing Target Specificity of Gene-Editing Nucleases and Deaminases.

Programmable nucleases and deaminases, which include zinc-finger nucleases, transcription activator-like effector nucleases, CRISPR RNA-guided nucleases, and RNA-guided base editors, are now widely employed for the targeted modification of genomes in cells and organisms. These gene-editing tools hold tremendous promise for therapeutic applications. Importantly, these nucleases and deaminases may display off-target activity through the recognition of near-cognate DNA sequences to their target sites, resulting in collateral damage to the genome in the form of local mutagenesis or genomic rearrangements. For therapeutic genome-editing applications with these classes of programmable enzymes, it is essential to measure and limit genome-wide off-target activity. Herein, we discuss the key determinants of off-target activity for these systems. We describe various cell-based and cell-free methods for identifying genome-wide off-target sites and diverse strategies that have been developed for reducing the off-target activity of programmable gene-editing enzymes.

Influence of pump laser fluence on ultrafast myoglobin structural dynamics.

High-intensity femtosecond pulses from an X-ray free-electron laser enable pump-probe experiments for the investigation of electronic and nuclear changes during light-induced reactions. On timescales ranging from femtoseconds to milliseconds and for a variety of biological systems, time-resolved serial femtosecond crystallography (TR-SFX) has provided detailed structural data for light-induced isomerization, breakage or formation of chemical bonds and electron transfer1,2. However, all ultrafast TR-SFX studies to date have employed such high pump laser energies that nominally several photons were absorbed per chromophore3-17. As multiphoton absorption may force the protein response into non-physiological pathways, it is of great concern18,19 whether this experimental approach20 allows valid conclusions to be drawn vis-à-vis biologically relevant single-photon-induced reactions18,19. Here we describe ultrafast pump-probe SFX experiments on the photodissociation of carboxymyoglobin, showing that different pump laser fluences yield markedly different results. In particular, the dynamics of structural changes and observed indicators of the mechanistically important coherent oscillations of the Fe-CO bond distance (predicted by recent quantum wavepacket dynamics21) are seen to depend strongly on pump laser energy, in line with quantum chemical analysis. Our results confirm both the feasibility and necessity of performing ultrafast TR-SFX pump-probe experiments in the linear photoexcitation regime. We consider this to be a starting point for reassessing both the design and the interpretation of ultrafast TR-SFX pump-probe experiments20 such that mechanistically relevant insight emerges.

Sources of gene expression variation in a globally diverse human cohort.

Genetic variation that influences gene expression and splicing is a key source of phenotypic diversity1-5. Although invaluable, studies investigating these links in humans have been strongly biased towards participants of European ancestries, which constrains generalizability and hinders evolutionary research. Here to address these limitations, we developed MAGE, an open-access RNA sequencing dataset of lymphoblastoid cell lines from 731 individuals from the 1000 Genomes Project6, spread across 5 continental groups and 26 populations. Most variation in gene expression (92%) and splicing (95%) was distributed within versus between populations, which mirrored the variation in DNA sequence. We mapped associations between genetic variants and expression and splicing of nearby genes (cis-expression quantitative trait loci (eQTLs) and cis-splicing QTLs (sQTLs), respectively). We identified more than 15,000 putatively causal eQTLs and more than 16,000 putatively causal sQTLs that are enriched for relevant epigenomic signatures. These include 1,310 eQTLs and 1,657 sQTLs that are largely private to underrepresented populations. Our data further indicate that the magnitude and direction of causal eQTL effects are highly consistent across populations. Moreover, the apparent 'population-specific' effects observed in previous studies were largely driven by low resolution or additional independent eQTLs of the same genes that were not detected. Together, our study expands our understanding of human gene expression diversity and provides an inclusive resource for studying the evolution and function of human genomes.

Effects and avoidance of photoconversion-induced artifacts in confocal and STED microscopy.

Fluorescence microscopy is limited by photoconversion due to continuous illumination, which results in not only photobleaching but also conversion of fluorescent molecules into species of different spectral properties through photoblueing. Here, we determined different fluorescence parameters of photoconverted products for various fluorophores under standard confocal and stimulated emission depletion (STED) microscopy conditions. We observed changes in both fluorescence spectra and lifetimes that can cause artifacts in quantitative measurements, which can be avoided by using exchangeable dyes.

The wooden artifacts from Schöningen's Spear Horizon and their place in human evolution.

Ethnographic records show that wooden tools played a pivotal role in the daily lives of hunter-gatherers including food procurement tools used in hunting (e.g., spears, throwing sticks) and gathering (e.g. digging sticks, bark peelers), as well as, domestic tools (e.g., handles, vessels). However, wood rarely survives in the archeological record, especially in Pleistocene contexts and knowledge of prehistoric hunter-gatherer lifeways is strongly biased by the survivorship of more resilient materials such as lithics and bones. Consequently, very few Paleolithic sites have produced wooden artifacts and among them, the site of Schöningen stands out due to its number and variety of wooden tools. The recovery of complete wooden spears and throwing sticks at this 300,000-y-old site (MIS 9) led to a paradigm shift in the hunter vs. scavenger debate. For the first time and almost 30 y after their discovery, this study introduces the complete wooden assemblage from Schöningen 13 II-4 known as the Spear Horizon. In total, 187 wooden artifacts could be identified from the Spear Horizon demonstrating a broad spectrum of wood-working techniques, including the splitting technique. A minimum of 20 hunting weapons is now recognized and two newly identified artifact types comprise 35 tools made on split woods, which were likely used in domestic activities. Schöningen 13 II-4 represents the largest Pleistocene wooden artifact assemblage worldwide and demonstrates the key role woodworking had in human evolution. Finally, our results considerably change the interpretation of the Pleistocene lakeshore site of Schöningen.

High-fidelity 3D live-cell nanoscopy through data-driven enhanced super-resolution radial fluctuation.

Live-cell super-resolution microscopy enables the imaging of biological structure dynamics below the diffraction limit. Here we present enhanced super-resolution radial fluctuations (eSRRF), substantially improving image fidelity and resolution compared to the original SRRF method. eSRRF incorporates automated parameter optimization based on the data itself, giving insight into the trade-off between resolution and fidelity. We demonstrate eSRRF across a range of imaging modalities and biological systems. Notably, we extend eSRRF to three dimensions by combining it with multifocus microscopy. This realizes live-cell volumetric super-resolution imaging with an acquisition speed of ~1 volume per second. eSRRF provides an accessible super-resolution approach, maximizing information extraction across varied experimental conditions while minimizing artifacts. Its optimal parameter prediction strategy is generalizable, moving toward unbiased and optimized analyses in super-resolution microscopy.

Materials and Structural Designs toward Motion Artifact-Free Bioelectronics.

Bioelectronics encompassing electronic components and circuits for accessing human information play a vital role in real-time and continuous monitoring of biophysiological signals of electrophysiology, mechanical physiology, and electrochemical physiology. However, mechanical noise, particularly motion artifacts, poses a significant challenge in accurately detecting and analyzing target signals. While software-based "postprocessing" methods and signal filtering techniques have been widely employed, challenges such as signal distortion, major requirement of accurate models for classification, power consumption, and data delay inevitably persist. This review presents an overview of noise reduction strategies in bioelectronics, focusing on reducing motion artifacts and improving the signal-to-noise ratio through hardware-based approaches such as "preprocessing". One of the main stress-avoiding strategies is reducing elastic mechanical energies applied to bioelectronics to prevent stress-induced motion artifacts. Various approaches including strain-compliance, strain-resistance, and stress-damping techniques using unique materials and structures have been explored. Future research should optimize materials and structure designs, establish stable processes and measurement methods, and develop techniques for selectively separating and processing overlapping noises. Ultimately, these advancements will contribute to the development of more reliable and effective bioelectronics for healthcare monitoring and diagnostics.

Evaluating drug targets through human loss-of-function genetic variation.

Naturally occurring human genetic variants that are predicted to inactivate protein-coding genes provide an in vivo model of human gene inactivation that complements knockout studies in cells and model organisms. Here we report three key findings regarding the assessment of candidate drug targets using human loss-of-function variants. First, even essential genes, in which loss-of-function variants are not tolerated, can be highly successful as targets of inhibitory drugs. Second, in most genes, loss-of-function variants are sufficiently rare that genotype-based ascertainment of homozygous or compound heterozygous 'knockout' humans will await sample sizes that are approximately 1,000 times those presently available, unless recruitment focuses on consanguineous individuals. Third, automated variant annotation and filtering are powerful, but manual curation remains crucial for removing artefacts, and is a prerequisite for recall-by-genotype efforts. Our results provide a roadmap for human knockout studies and should guide the interpretation of loss-of-function variants in drug development.

Virtual blebbistatin: A robust and rapid software approach to motion artifact removal in optical mapping of cardiomyocytes.

Fluorescent reporters of cardiac electrophysiology provide valuable information on heart cell and tissue function. However, motion artifacts caused by cardiac muscle contraction interfere with accurate measurement of fluorescence signals. Although drugs such as blebbistatin can be applied to stop cardiac tissue from contracting by uncoupling calcium-contraction, their usage prevents the study of excitation-contraction coupling and, as we show, impacts cellular structure. We therefore developed a robust method to remove motion computationally from images of contracting cardiac muscle and to map fluorescent reporters of cardiac electrophysiological activity onto images of undeformed tissue. When validated on cardiomyocytes derived from human induced pluripotent stem cells (iPSCs), in both monolayers and engineered tissues, the method enabled efficient and robust reduction of motion artifact. As with pharmacologic approaches using blebbistatin for motion removal, our algorithm improved the accuracy of optical mapping, as demonstrated by spatial maps of calcium transient decay. However, unlike pharmacologic motion removal, our computational approach allowed direct analysis of calcium-contraction coupling. Results revealed calcium-contraction coupling to be more uniform across cells within engineered tissues than across cells in monolayer culture. The algorithm shows promise as a robust and accurate tool for optical mapping studies of excitation-contraction coupling in heart tissue.

Fluorescent protein tagging promotes phase separation and alters the aggregation pathway of huntingtin exon-1.

Fluorescent protein tags are convenient tools for tracking the aggregation states of amyloidogenic or phase separating proteins, but the effect of the tags is often not well understood. Here, we investigated the impact of a C-terminal red fluorescent protein (RFP) tag on the phase separation of huntingtin exon-1 (Httex1), an N-terminal portion of the huntingtin protein that aggregates in Huntington's disease. We found that the RFP-tagged Httex1 rapidly formed micron-sized, phase separated states in the presence of a crowding agent. The formed structures had a rounded appearance and were highly dynamic according to electron paramagnetic resonance and fluorescence recovery after photobleaching, suggesting that the phase separated state was largely liquid in nature. Remarkably, the untagged protein did not undergo any detectable liquid condensate formation under the same conditions. In addition to strongly promoting liquid-liquid phase separation, the RFP tag also facilitated fibril formation, as the tag-dependent liquid condensates rapidly underwent a liquid-to-solid transition. The rate of fibril formation under these conditions was significantly faster than that of the untagged protein. When expressed in cells, the RFP-tagged Httex1 formed larger aggregates with different antibody staining patterns compared to untagged Httex1. Collectively, these data reveal that the addition of a fluorescent protein tag significantly impacts liquid and solid phase separations of Httex1 in vitro and leads to altered aggregation in cells. Considering that the tagged Httex1 is commonly used to study the mechanisms of Httex1 misfolding and toxicity, our findings highlight the importance to validate the conclusions with untagged protein.

DeepProjection: specific and robust projection of curved 2D tissue sheets from 3D microscopy using deep learning.

The efficient extraction of image data from curved tissue sheets embedded in volumetric imaging data remains a serious and unsolved problem in quantitative studies of embryogenesis. Here, we present DeepProjection (DP), a trainable projection algorithm based on deep learning. This algorithm is trained on user-generated training data to locally classify 3D stack content, and to rapidly and robustly predict binary masks containing the target content, e.g. tissue boundaries, while masking highly fluorescent out-of-plane artifacts. A projection of the masked 3D stack then yields background-free 2D images with undistorted fluorescence intensity values. The binary masks can further be applied to other fluorescent channels or to extract local tissue curvature. DP is designed as a first processing step than can be followed, for example, by segmentation to track cell fate. We apply DP to follow the dynamic movements of 2D-tissue sheets during dorsal closure in Drosophila embryos and of the periderm layer in the elongating Danio embryo. DeepProjection is available as a fully documented Python package.

Optimal precision and accuracy in 4Pi-STORM using dynamic spline PSF models.

Coherent fluorescence imaging with two objective lenses (4Pi detection) enables single-molecule localization microscopy with sub-10 nm spatial resolution in three dimensions. Despite its outstanding sensitivity, wider application of this technique has been hindered by complex instrumentation and the challenging nature of the data analysis. Here we report the development of a 4Pi-STORM microscope, which obtains optimal resolution and accuracy by modeling the 4Pi point spread function (PSF) dynamically while also using a simpler optical design. Dynamic spline PSF models incorporate fluctuations in the modulation phase of the experimentally determined PSF, capturing the temporal evolution of the optical system. Our method reaches the theoretical limits for precision and minimizes phase-wrapping artifacts by making full use of the information content of the data. 4Pi-STORM achieves a near-isotropic three-dimensional localization precision of 2-3 nm, and we demonstrate its capabilities by investigating protein and nucleic acid organization in primary neurons and mammalian mitochondria.

Incorporating the image formation process into deep learning improves network performance.

We present Richardson-Lucy network (RLN), a fast and lightweight deep learning method for three-dimensional fluorescence microscopy deconvolution. RLN combines the traditional Richardson-Lucy iteration with a fully convolutional network structure, establishing a connection to the image formation process and thereby improving network performance. Containing only roughly 16,000 parameters, RLN enables four- to 50-fold faster processing than purely data-driven networks with many more parameters. By visual and quantitative analysis, we show that RLN provides better deconvolution, better generalizability and fewer artifacts than other networks, especially along the axial dimension. RLN outperforms classic Richardson-Lucy deconvolution on volumes contaminated with severe out of focus fluorescence or noise and provides four- to sixfold faster reconstructions of large, cleared-tissue datasets than classic multi-view pipelines. We demonstrate RLN's performance on cells, tissues and embryos imaged with widefield-, light-sheet-, confocal- and super-resolution microscopy.

Artifacts and biases of the reverse transcription reaction in RNA sequencing.

RNA sequencing has spurred a significant number of research areas in recent years. Most protocols rely on synthesizing a more stable complementary DNA (cDNA) copy of the RNA molecule during the reverse transcription reaction. The resulting cDNA pool is often wrongfully assumed to be quantitatively and molecularly similar to the original RNA input. Sadly, biases and artifacts confound the resulting cDNA mixture. These issues are often overlooked or ignored in the literature by those that rely on the reverse transcription process. In this review, we confront the reader with intra- and intersample biases and artifacts caused by the reverse transcription reaction during RNA sequencing experiments. To fight the reader's despair, we also provide solutions to most issues and inform on good RNA sequencing practices. We hope the reader can use this review to their advantage, thereby contributing to scientifically sound RNA studies.

Reanalysis of single-cell RNA sequencing data does not support herpes simplex virus 1 latency in non-neuronal ganglionic cells in mice.

Most individuals are latently infected with herpes simplex virus type 1 (HSV-1), and it is well-established that HSV-1 establishes latency in sensory neurons of peripheral ganglia. However, it was recently proposed that latent HSV-1 is also present in immune cells recovered from the ganglia of experimentally infected mice. Here, we reanalyzed the single-cell RNA sequencing (scRNA-Seq) data that formed the basis for that conclusion. Unexpectedly, off-target priming in 3' scRNA-Seq experiments enabled the detection of non-polyadenylated HSV-1 latency-associated transcript (LAT) intronic RNAs. However, LAT reads were near-exclusively detected in mixed populations of cells undergoing cell death. Specific loss of HSV-1 LAT and neuronal transcripts during quality control filtering indicated widespread destruction of neurons, supporting the presence of contaminating cell-free RNA in other cells following tissue processing. In conclusion, the reported detection of latent HSV-1 in non-neuronal cells is best explained using compromised scRNA-Seq datasets.IMPORTANCEMost people are infected with herpes simplex virus type 1 (HSV-1) during their life. Once infected, the virus generally remains in a latent (silent) state, hiding within the neurons of peripheral ganglia. Periodic reactivation (reawakening) of the virus may cause fresh diseases such as cold sores. A recent study using single-cell RNA sequencing (scRNA-Seq) proposed that HSV-1 can also establish latency in the immune cells of mice, challenging existing dogma. We reanalyzed the data from that study and identified several flaws in the methodologies and analyses performed that invalidate the published conclusions. Specifically, we showed that the methodologies used resulted in widespread destruction of neurons which resulted in the presence of contaminants that confound the data analysis. We thus conclude that there remains little to no evidence for HSV-1 latency in immune cells.

Macrophage heterogeneity in the single-cell era: facts and artifacts.

In this spotlight, we review technical issues that compromise single-cell analysis of tissue macrophages, including limited and unrepresentative yields, fragmentation and generation of remnants, and activation during tissue disaggregation. These issues may lead to a misleading definition of subpopulations of macrophages and the expression of macrophage-specific transcripts by unrelated cells. Recognition of the technical limitations of single-cell approaches is required in order to map the full spectrum of tissue-resident macrophage heterogeneity and assess its biological significance.

Reanalysis shows there is not an extreme decline effect in fish ocean acidification studies.

This Formal Comment uses re-analysis after appropriate corrections to claim that the extreme decline effect reported by Clements et al. is a statistical artefact caused by the way they corrected for zeros in percentage data, exacerbated by errors in data compilation, selective data inclusions and missing studies with strong effects.

Knotted artifacts in predicted 3D RNA structures.

Unlike proteins, RNAs deposited in the Protein Data Bank do not contain topological knots. Recently, admittedly, the first trefoil knot and some lasso-type conformations have been found in experimental RNA structures, but these are still exceptional cases. Meanwhile, algorithms predicting 3D RNA models have happened to form knotted structures not so rarely. Interestingly, machine learning-based predictors seem to be more prone to generate knotted RNA folds than traditional methods. A similar situation is observed for the entanglements of structural elements. In this paper, we analyze all models submitted to the CASP15 competition in the 3D RNA structure prediction category. We show what types of topological knots and structure element entanglements appear in the submitted models and highlight what methods are behind the generation of such conformations. We also study the structural aspect of susceptibility to entanglement. We suggest that predictors take care of an evaluation of RNA models to avoid publishing structures with artifacts, such as unusual entanglements, that result from hallucinations of predictive algorithms.

Removing direct photocurrent artifacts in optogenetic connectivity mapping data via constrained matrix factorization.

Monosynaptic connectivity mapping is crucial for building circuit-level models of neural computation. Two-photon optogenetic stimulation, when combined with whole-cell recording, enables large-scale mapping of physiological circuit parameters. In this experimental setup, recorded postsynaptic currents are used to infer the presence and strength of connections. For many cell types, nearby connections are those we expect to be strongest. However, when the postsynaptic cell expresses opsin, optical excitation of nearby cells can induce direct photocurrents in the postsynaptic cell. These photocurrent artifacts contaminate synaptic currents, making it difficult or impossible to probe connectivity for nearby cells. To overcome this problem, we developed a computational tool, Photocurrent Removal with Constraints (PhoRC). Our method is based on a constrained matrix factorization model which leverages the fact that photocurrent kinetics are less variable than those of synaptic currents. We demonstrate on real and simulated data that PhoRC consistently removes photocurrents while preserving synaptic currents, despite variations in photocurrent kinetics across datasets. Our method allows the discovery of synaptic connections which would have been otherwise obscured by photocurrent artifacts, and may thus reveal a more complete picture of synaptic connectivity. PhoRC runs faster than real time and is available as open source software.

Continuous evaluation of denoising strategies in resting-state fMRI connectivity using fMRIPrep and Nilearn.

Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a denoising benchmark featuring a range of denoising strategies, datasets and evaluation metrics for connectivity analyses, based on the popular fMRIprep software. The benchmark prototypes an implementation of a reproducible framework, where the provided Jupyter Book enables readers to reproduce or modify the figures on the Neurolibre reproducible preprint server (https://neurolibre.org/). We demonstrate how such a reproducible benchmark can be used for continuous evaluation of research software, by comparing two versions of the fMRIprep. Most of the benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing was generally effective, but is incompatible with statistical analyses requiring the continuous sampling of brain signal, for which a simpler strategy, using motion parameters, average activity in select brain compartments, and global signal regression, is preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. This work will hopefully provide useful guidelines for the fMRIprep users community, and highlight the importance of continuous evaluation of research methods.