Understanding the cell: Future views of structural biology.

Determining the structure and mechanisms of all individual functional modules of cells at high molecular detail has often been seen as equal to understanding how cells work. Recent technical advances have led to a flush of high-resolution structures of various macromolecular machines, but despite this wealth of detailed information, our understanding of cellular function remains incomplete. Here, we discuss present-day limitations of structural biology and highlight novel technologies that may enable us to analyze molecular functions directly inside cells. We predict that the progression toward structural cell biology will involve a shift toward conceptualizing a 4D virtual reality of cells using digital twins. These will capture cellular segments in a highly enriched molecular detail, include dynamic changes, and facilitate simulations of molecular processes, leading to novel and experimentally testable predictions. Transferring biological questions into algorithms that learn from the existing wealth of data and explore novel solutions may ultimately unveil how cells work.

A programmable reaction-diffusion system for spatiotemporal cell signaling circuit design.

Cells self-organize molecules in space and time to generate complex behaviors, but we lack synthetic strategies for engineering spatiotemporal signaling. We present a programmable reaction-diffusion platform for designing protein oscillations, patterns, and circuits in mammalian cells using two bacterial proteins, MinD and MinE (MinDE). MinDE circuits act like "single-cell radios," emitting frequency-barcoded fluorescence signals that can be spectrally isolated and analyzed using digital signal processing tools. We define how to genetically program these signals and connect their spatiotemporal dynamics to cell biology using engineerable protein-protein interactions. This enabled us to construct sensitive reporter circuits that broadcast endogenous cell signaling dynamics on a frequency-barcoded imaging channel and to build control signal circuits that synthetically pattern activities in the cell, such as protein condensate assembly and actin filamentation. Our work establishes a paradigm for visualizing, probing, and engineering cellular activities at length and timescales critical for biological function.

Two-photon synthetic aperture microscopy for minimally invasive fast 3D imaging of native subcellular behaviors in deep tissue.

Holistic understanding of physio-pathological processes requires noninvasive 3D imaging in deep tissue across multiple spatial and temporal scales to link diverse transient subcellular behaviors with long-term physiogenesis. Despite broad applications of two-photon microscopy (TPM), there remains an inevitable tradeoff among spatiotemporal resolution, imaging volumes, and durations due to the point-scanning scheme, accumulated phototoxicity, and optical aberrations. Here, we harnessed the concept of synthetic aperture radar in TPM to achieve aberration-corrected 3D imaging of subcellular dynamics at a millisecond scale for over 100,000 large volumes in deep tissue, with three orders of magnitude reduction in photobleaching. With its advantages, we identified direct intercellular communications through migrasome generation following traumatic brain injury, visualized the formation process of germinal center in the mouse lymph node, and characterized heterogeneous cellular states in the mouse visual cortex, opening up a horizon for intravital imaging to understand the organizations and functions of biological systems at a holistic level.

Direct Promoter Repression by BCL11A Controls the Fetal to Adult Hemoglobin Switch.

Fetal hemoglobin (HbF, α2γ2) level is genetically controlled and modifies severity of adult hemoglobin (HbA, α2ß2) disorders, sickle cell disease, and ß-thalassemia. Common genetic variation affects expression of BCL11A, a regulator of HbF silencing. To uncover how BCL11A supports the developmental switch from γ- to ß- globin, we use a functional assay and protein binding microarray to establish a requirement for a zinc-finger cluster in BCL11A in repression and identify a preferred DNA recognition sequence. This motif appears in embryonic and fetal-expressed globin promoters and is duplicated in γ-globin promoters. The more distal of the duplicated motifs is mutated in individuals with hereditary persistence of HbF. Using the CUT&RUN approach to map protein binding sites in erythroid cells, we demonstrate BCL11A occupancy preferentially at the distal motif, which can be disrupted by editing the promoter. Our findings reveal that direct γ-globin gene promoter repression by BCL11A underlies hemoglobin switching.

Proximal termination generates a transcriptional state that determines the rate of establishment of Polycomb silencing.

The mechanisms and timescales controlling de novo establishment of chromatin-mediated transcriptional silencing by Polycomb repressive complex 2 (PRC2) are unclear. Here, we investigate PRC2 silencing at Arabidopsis FLOWERING LOCUS C (FLC), known to involve co-transcriptional RNA processing, histone demethylation activity, and PRC2 function, but so far not mechanistically connected. We develop and test a computational model describing proximal polyadenylation/termination mediated by the RNA-binding protein FCA that induces H3K4me1 removal by the histone demethylase FLD. H3K4me1 removal feeds back to reduce RNA polymerase II (RNA Pol II) processivity and thus enhance early termination, thereby repressing productive transcription. The model predicts that this transcription-coupled repression controls the level of transcriptional antagonism to PRC2 action. Thus, the effectiveness of this repression dictates the timescale for establishment of PRC2/H3K27me3 silencing. We experimentally validate these mechanistic model predictions, revealing that co-transcriptional processing sets the level of productive transcription at the locus, which then determines the rate of the ON-to-OFF switch to PRC2 silencing.

Cancer misinformation on social media.

Social media is widely used globally by patients, families of patients, health professionals, scientists, and other stakeholders who seek and share information related to cancer. Despite many benefits of social media for cancer care and research, there is also a substantial risk of exposure to misinformation, or inaccurate information about cancer. Types of misinformation vary from inaccurate information about cancer risk factors or unproven treatment options to conspiracy theories and public relations articles or advertisements appearing as reliable medical content. Many characteristics of social media networks-such as their extensive use and the relative ease it allows to share information quickly-facilitate the spread of misinformation. Research shows that inaccurate and misleading health-related posts on social media often get more views and engagement (e.g., likes, shares) from users compared with accurate information. Exposure to misinformation can have downstream implications for health-related attitudes and behaviors. However, combatting misinformation is a complex process that requires engagement from media platforms, scientific and health experts, governmental organizations, and the general public. Cancer experts, for example, should actively combat misinformation in real time and should disseminate evidence-based content on social media. Health professionals should give information prescriptions to patients and families and support health literacy. Patients and families should vet the quality of cancer information before acting upon it (e.g., by using publicly available checklists) and seek recommended resources from health care providers and trusted organizations. Future multidisciplinary research is needed to identify optimal ways of building resilience and combating misinformation across social media.

Health Digital Twins in Life Science and Health Care Innovation.

Health digital twins (HDTs) are virtual representations of real individuals that can be used to simulate human physiology, disease, and drug effects. HDTs can be used to improve drug discovery and development by providing a data-driven approach to inform target selection, drug delivery, and design of clinical trials. HDTs also offer new applications into precision therapies and clinical decision making. The deployment of HDTs at scale could bring a precision approach to public health monitoring and intervention. Next steps include challenges such as addressing socioeconomic barriers and ensuring the representativeness of the technology based on the training and validation data sets. Governance and regulation of HDT technology are still in the early stages.

Assessing autosomal aneuploidy in ancient genomes.

Using genetic methods, aneuploidies can be detected in ancient human remains, which is so far the only way to reliably prove their existence in the past. As highlighted in recent studies by Rohrlach et al. and by Anastasiadou et al., this initial step enables a deeper exploration of the history of rare diseases, encompassing the social and historical contexts of the afflicted individuals.

Topological analysis of 3D digital ovules identifies cellular patterns associated with ovule shape diversity.

Tissue morphogenesis remains poorly understood. In plants, a central problem is how the 3D cellular architecture of a developing organ contributes to its final shape. We address this question through a comparative analysis of ovule morphogenesis, taking advantage of the diversity in ovule shape across angiosperms. Here, we provide a 3D digital atlas of Cardamine hirsuta ovule development at single cell resolution and compare it with an equivalent atlas of Arabidopsis thaliana. We introduce nerve-based topological analysis as a tool for unbiased detection of differences in cellular architectures and corroborate identified topological differences between two homologous tissues by comparative morphometrics and visual inspection. We find that differences in topology, cell volume variation and tissue growth patterns in the sheet-like integuments and the bulbous chalaza are associated with differences in ovule curvature. In contrast, the radialized conical ovule primordia and nucelli exhibit similar shapes, despite differences in internal cellular topology and tissue growth patterns. Our results support the notion that the structural organization of a tissue is associated with its susceptibility to shape changes during evolutionary shifts in 3D cellular architecture.

A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context.

We present a new set of computational tools that enable accurate and widely applicable 3D segmentation of nuclei in various 3D digital organs. We have developed an approach for ground truth generation and iterative training of 3D nuclear segmentation models, which we applied to popular CellPose, PlantSeg and StarDist algorithms. We provide two high-quality models trained on plant nuclei that enable 3D segmentation of nuclei in datasets obtained from fixed or live samples, acquired from different plant and animal tissues, and stained with various nuclear stains or fluorescent protein-based nuclear reporters. We also share a diverse high-quality training dataset of about 10,000 nuclei. Furthermore, we advanced the MorphoGraphX analysis and visualization software by, among other things, providing a method for linking 3D segmented nuclei to their surrounding cells in 3D digital organs. We found that the nuclear-to-cell volume ratio varies between different ovule tissues and during the development of a tissue. Finally, we extended the PlantSeg 3D segmentation pipeline with a proofreading tool that uses 3D segmented nuclei as seeds to correct cell segmentation errors in difficult-to-segment tissues.

Single-Nucleotide-Resolution Computing and Memory in Living Cells.

The ability to process and store information in living cells is essential for developing next-generation therapeutics and studying biology in situ. However, existing strategies have limited recording capacity and are challenging to scale. To overcome these limitations, we developed DOMINO, a robust and scalable platform for encoding logic and memory in bacterial and eukaryotic cells. Using an efficient single-nucleotide-resolution Read-Write head for DNA manipulation, DOMINO converts the living cells' DNA into an addressable, readable, and writable medium for computation and storage. DOMINO operators enable analog and digital molecular recording for long-term monitoring of signaling dynamics and cellular events. Furthermore, multiple operators can be layered and interconnected to encode order-independent, sequential, and temporal logic, allowing recording and control over the combination, order, and timing of molecular events in cells. We envision that DOMINO will lay the foundation for building robust and sophisticated computation-and-memory gene circuits for numerous biotechnological and biomedical applications.

StratoLAMP: Label-free, multiplex digital loop-mediated isothermal amplification based on visual stratification of precipitate.

Multiplex, digital nucleic acid detections have important biomedical applications, but the multiplexity of existing methods is predominantly achieved using fluorescent dyes or probes, making the detection complicated and costly. Here, we present the StratoLAMP for label-free, multiplex digital loop-mediated isothermal amplification based on visual stratification of the precipitate byproduct. The StratoLAMP designates two sets of primers with different concentrations to achieve different precipitate yields when amplifying different nucleic acid targets. In the detection, deep learning image analysis is used to stratify the precipitate within each droplet and determine the encapsulated targets for nucleic acid quantification. We investigated the effect of the amplification reagents and process on the precipitate generation and optimized the assay conditions. We then implemented a deep-learning image analysis pipeline for droplet detection, achieving an overall accuracy of 94.3%. In the application, the StratoLAMP successfully achieved the simultaneous quantification of two nucleic acid targets with high accuracy. By eliminating the need for fluorescence, StratoLAMP represents a unique concept toward label-free, multiplex nucleic acid assays and an analytical tool with great cost-effectiveness.

Role of molecular damage in crack initiation mechanisms of tough elastomers.

Tough soft materials such as multiple network elastomers (MNE) or filled elastomers are typically stretchable and include significant energy dissipation mechanisms that prevent or delay crack growth. Yet most studies and fracture models focus on steady-state propagation and damage is assumed to be decoupled from the local stress and strain fields near the crack tip. We report an in situ spatial-temporally resolved 3D measurement of molecular damage in mechanophore-labeled MNE just before a crack propagates. This technique, complemented by digital image correlation, allows us to compare the spatial distribution of both damage and deformation in single network (SN) elastomers and in MNE. Compared to SN, MNE have a wide-spread damage in front of the crack and, surprisingly, delocalize strain concentration. A continuum model, where damage distribution is fully coupled to the crack tip fields, is proposed to explain these results. Additional measurements of time-dependent molecular damage during fixed grips relaxation in the presence of a crack reveal that the less localized damage distribution delays fracture initiation. The observations and exploratory modeling reveal the dynamic fracture mechanism of MNE, providing guidance for rational design of high-performance tough elastomers.

Path dependence, stigmergy, and memetic reification in the formation of the 27 Club myth.

The "27 Club" refers to the widespread legend that notable people, particularly musicians, are unusually likely to die at age 27. A 2011 inquiry in The BMJ showed this is not the case, dismissing the 27 Club as a myth. We expand on this discourse by demonstrating that although the existence of the phenomenon cannot be empirically validated, it is real in its consequences. Using Wikipedia data, we show that while age 27 does not hold greater risk of mortality for notable persons, those who died at 27 are as a group exceptionally notable compared to those who died at other young ages. The 27 Club legend originated from a statistically improbable event circa 1970, wherein four superstar musicians died within the span of 2 y all at age 27. This coincidence captured the public imagination such that our fascination with the 27 Club brought itself into being, producing greater interest in those who died at age 27 than would have been otherwise. This demonstrates path dependence in cultural evolution, whereby an effectively random event evolves into a narrative that shapes otherwise unrelated events and thus the way we make and interpret history.

Do digital technologies reduce racially biased reporting? Evidence from NYPD administrative data.

Recent work has emphasized the disproportionate bias faced by minorities when interacting with law enforcement. However, research on the topic has been hampered by biased sampling in administrative data, namely that records of police interactions with citizens only reflect information on the civilians that police elect to investigate, and not civilians that police observe but do not investigate. In this work, we address a related bias in administrative police data which has received less empirical attention, namely reporting biases around investigations that have taken place. Further, we investigate whether digital monitoring tools help mitigate this reporting bias. To do so, we examine changes in reports of interactions between law enforcement and citizens in the wake of the New York City Police Department's replacement of analog memo books with mobile smartphones. Results from a staggered difference in differences estimation indicate a significant increase in reports of citizen stops once the new smartphones are deployed. Importantly, we observe that the rise is driven by increased reports of "unproductive" stops, stops involving non-White citizens, and stops occurring in areas characterized by a greater concentration of crime and non-White residents. These results reinforce the recent observation that prior work has likely underestimated the extent of racial bias in policing. Further, they highlight that the implementation of digital monitoring tools can mitigate the issue to some extent.

A simplicial epidemic model for COVID-19 spread analysis.

Networks allow us to describe a wide range of interaction phenomena that occur in complex systems arising in such diverse fields of knowledge as neuroscience, engineering, ecology, finance, and social sciences. Until very recently, the primary focus of network models and tools has been on describing the pairwise relationships between system entities. However, increasingly more studies indicate that polyadic or higher-order group relationships among multiple network entities may be the key toward better understanding of the intrinsic mechanisms behind the functionality of complex systems. Such group interactions can be, in turn, described in a holistic manner by simplicial complexes of graphs. Inspired by these recently emerging results on the utility of the simplicial geometry of complex networks for contagion propagation and armed with a large-scale synthetic social contact network (also known as a digital twin) of the population in the U.S. state of Virginia, in this paper, we aim to glean insights into the role of higher-order social interactions and the associated varying social group determinants on COVID-19 propagation and mitigation measures.

Toward the quantification of α-synuclein aggregates with digital seed amplification assays.

The quantification and characterization of aggregated α-synuclein in clinical samples offer immense potential toward diagnosing, treating, and better understanding neurodegenerative synucleinopathies. Here, we developed digital seed amplification assays to detect single α-synuclein aggregates by partitioning the reaction into microcompartments. Using pre-formed α-synuclein fibrils as reaction seeds, we measured aggregate concentrations as low as 4 pg/mL. To improve our sensitivity, we captured aggregates on antibody-coated magnetic beads before running the amplification reaction. By first characterizing the pre-formed fibrils with transmission electron microscopy and size exclusion chromatography, we determined the specific aggregates targeted by each assay platform. Using brain tissue and cerebrospinal fluid samples collected from patients with Parkinson's Disease and multiple system atrophy, we demonstrated that the assay can detect endogenous pathological α-synuclein aggregates. Furthermore, as another application for these assays, we studied the inhibition of α-synuclein aggregation in the presence of small-molecule inhibitors and used a custom image analysis pipeline to quantify changes in aggregate growth and filament morphology.

DNA-DISK: Automated end-to-end data storage via enzymatic single-nucleotide DNA synthesis and sequencing on digital microfluidics.

In the age of information explosion, the exponential growth of digital data far exceeds the capacity of current mainstream storage media. DNA is emerging as a promising alternative due to its higher storage density, longer retention time, and lower power consumption. To date, commercially mature DNA synthesis and sequencing technologies allow for writing and reading of information on DNA with customization and convenience at the research level. However, under the disconnected and nonspecialized mode, DNA data storage encounters practical challenges, including susceptibility to errors, long storage latency, resource-intensive requirements, and elevated information security risks. Herein, we introduce a platform named DNA-DISK that seamlessly streamlined DNA synthesis, storage, and sequencing on digital microfluidics coupled with a tabletop device for automated end-to-end information storage. The single-nucleotide enzymatic DNA synthesis with biocapping strategy is utilized, offering an ecofriendly and cost-effective approach for data writing. A DNA encapsulation using thermo-responsive agarose is developed for on-chip solidification, not only eliminating data clutter but also preventing DNA degradation. Pyrosequencing is employed for in situ and accurate data reading. As a proof of concept, DNA-DISK successfully stored and retrieved a musical sheet file (228 bits) with lower write-to-read latency (4.4 min of latency per bit) as well as superior automation compared to other platforms, demonstrating its potential to evolve into a DNA Hard Disk Drive in the future.

The NYCKidSeq randomized controlled trial: Impact of GUÍA digitally enhanced genetic results disclosure in diverse families.

Digital solutions are needed to support rapid increases in the application of genetic/genomic tests (GTs) in diverse clinical settings and patient populations. We developed GUÍA, a bilingual digital application that facilitates disclosure of GT results. The NYCKidSeq randomized controlled trial enrolled diverse children with neurologic, cardiac, and immunologic conditions who underwent GTs. The trial evaluated GUÍA's impact on understanding the GT results by randomizing families to results disclosure genetic counseling with GUÍA (intervention) or standard of care (SOC). Parents/legal guardians (participants) completed surveys at baseline, post-results disclosure, and 6 months later. Survey measures assessed the primary study outcomes of participants' perceived understanding of and confidence in explaining their child's GT results and the secondary outcome of objective understanding. The analysis included 551 diverse participants, 270 in the GUÍA arm and 281 in SOC. Participants in the GUÍA arm had significantly higher perceived understanding post-results (OR = 2.8, CI[1.004, 7.617], p = 0.049) and maintained higher objective understanding over time (OR = 1.1, CI[1.004, 1.127], p = 0.038) compared to SOC. There was no impact on perceived confidence. Hispanic/Latino(a) individuals in the GUÍA arm maintained higher perceived understanding (OR = 3.9, CI[1.603, 9.254], p = 0.003), confidence (OR = 2.7, CI[1.021, 7.277], p = 0.046), and objective understanding (OR = 1.1, CI[1.009, 1.212], p = 0.032) compared to SOC. This trial demonstrates that GUÍA positively impacts understanding of GT results in diverse parents of children with suspected genetic conditions and builds a case for utilizing GUÍA to deliver complex results. Continued development and evaluation of digital applications in diverse populations are critical for equitably scaling GT offerings in specialty clinics.

Digital PCR threshold robustness analysis and optimization using dipcensR.

Digital polymerase chain reaction (dPCR) is a best-in-class molecular biology technique for the accurate and precise quantification of nucleic acids. The recent maturation of dPCR technology allows the quantification of up to thousands of targeted nucleic acids per instrument per day. A key step in the dPCR data analysis workflow is the classification of partitions into two classes based on their partition intensities: partitions either containing or lacking target nucleic acids of interest. Much effort has been invested in the design and tailoring of automated dPCR partition classification procedures, and such procedures will be increasingly important as the technology ventures into high-throughput applications. However, automated partition classification is not fail-safe, and evaluation of its accuracy is highly advised. This accuracy evaluation is a manual endeavor and is becoming a bottleneck for high-throughput dPCR applications. Here, we introduce dipcensR, the first data-analysis procedure that automates the assessment of any linear partition classifier's partition classification accuracy, offering potentially substantial efficiency gains. dipcensR is based on a robustness evaluation of said partition classification and flags classifications with low robustness as needing review. Additionally, dipcensR's robustness analysis underpins (optional) automatic optimization of partition classification to achieve maximal robustness. A freely available R implementation supports dipcensR's use.