Phage-assisted evolution and protein engineering yield compact, efficient prime editors.

Prime editing enables a wide variety of precise genome edits in living cells. Here we use protein evolution and engineering to generate prime editors with reduced size and improved efficiency. Using phage-assisted evolution, we improved editing efficiencies of compact reverse transcriptases by up to 22-fold and generated prime editors that are 516-810 base pairs smaller than the current-generation editor PEmax. We discovered that different reverse transcriptases specialize in different types of edits and used this insight to generate reverse transcriptases that outperform PEmax and PEmaxΔRNaseH, the truncated editor used in dual-AAV delivery systems. Finally, we generated Cas9 domains that improve prime editing. These resulting editors (PE6a-g) enhance therapeutically relevant editing in patient-derived fibroblasts and primary human T-cells. PE6 variants also enable longer insertions to be installed in vivo following dual-AAV delivery, achieving 40% loxP insertion in the cortex of the murine brain, a 24-fold improvement compared to previous state-of-the-art prime editors.

Influence of autozygosity on common disease risk across the phenotypic spectrum.

Autozygosity is associated with rare Mendelian disorders and clinically relevant quantitative traits. We investigated associations between the fraction of the genome in runs of homozygosity (FROH) and common diseases in Genes & Health (n = 23,978 British South Asians), UK Biobank (n = 397,184), and 23andMe. We show that restricting analysis to offspring of first cousins is an effective way of reducing confounding due to social/environmental correlates of FROH. Within this group in G&H+UK Biobank, we found experiment-wide significant associations between FROH and twelve common diseases. We replicated associations with type 2 diabetes (T2D) and post-traumatic stress disorder via within-sibling analysis in 23andMe (median n = 480,282). We estimated that autozygosity due to consanguinity accounts for 5%-18% of T2D cases among British Pakistanis. Our work highlights the possibility of widespread non-additive genetic effects on common diseases and has important implications for global populations with high rates of consanguinity.

Lack of affinity signature for germinal center cells that have initiated plasma cell differentiation.

Long-lived plasma cells (PCs) secrete antibodies that can provide sustained immunity against infection. High-affinity cells are proposed to preferentially select into this compartment, potentiating the immune response. We used single-cell RNA-seq to track the germinal center (GC) development of Ighg2A10 B cells, specific for the Plasmodium falciparum circumsporozoite protein (PfCSP). Following immunization with Plasmodium sporozoites, we identified 3 populations of cells in the GC light zone (LZ). One LZ population expressed a gene signature associated with the initiation of PC differentiation and readily formed PCs in vitro. The estimated affinity of these pre-PC B cells was indistinguishable from that of LZ cells that remained in the GC. This remained true when high- or low-avidity recombinant PfCSP proteins were used as immunogens. These findings suggest that the initiation of PC development occurs via an affinity-independent process.

Prospectively Isolated Tetraspanin+ Neoblasts Are Adult Pluripotent Stem Cells Underlying Planaria Regeneration.

Proliferating cells known as neoblasts include pluripotent stem cells (PSCs) that sustain tissue homeostasis and regeneration of lost body parts in planarians. However, the lack of markers to prospectively identify and isolate these adult PSCs has significantly hampered their characterization. We used single-cell RNA sequencing (scRNA-seq) and single-cell transplantation to address this long-standing issue. Large-scale scRNA-seq of sorted neoblasts unveiled a novel subtype of neoblast (Nb2) characterized by high levels of PIWI-1 mRNA and protein and marked by a conserved cell-surface protein-coding gene, tetraspanin 1 (tspan-1). tspan-1-positive cells survived sub-lethal irradiation, underwent clonal expansion to repopulate whole animals, and when purified with an anti-TSPAN-1 antibody, rescued the viability of lethally irradiated animals after single-cell transplantation. The first prospective isolation of an adult PSC bridges a conceptual dichotomy between functionally and molecularly defined neoblasts, shedding light on mechanisms governing in vivo pluripotency and a source of regeneration in animals. VIDEO ABSTRACT.

Physiologic Medium Rewires Cellular Metabolism and Reveals Uric Acid as an Endogenous Inhibitor of UMP Synthase.

A complex interplay of environmental factors impacts the metabolism of human cells, but neither traditional culture media nor mouse plasma mimic the metabolite composition of human plasma. Here, we developed a culture medium with polar metabolite concentrations comparable to those of human plasma (human plasma-like medium [HPLM]). Culture in HPLM, relative to that in traditional media, had widespread effects on cellular metabolism, including on the metabolome, redox state, and glucose utilization. Among the most prominent was an inhibition of de novo pyrimidine synthesis-an effect traced to uric acid, which is 10-fold higher in the blood of humans than of mice and other non-primates. We find that uric acid directly inhibits uridine monophosphate synthase (UMPS) and consequently reduces the sensitivity of cancer cells to the chemotherapeutic agent 5-fluorouracil. Thus, media that better recapitulates the composition of human plasma reveals unforeseen metabolic wiring and regulation, suggesting that HPLM should be of broad utility.

Patient privacy in AI-driven omics methods.

Artificial intelligence (AI) in omics analysis raises privacy threats to patients. Here, we briefly discuss risk factors to patient privacy in data sharing, model training, and release, as well as methods to safeguard and evaluate patient privacy in AI-driven omics methods.

Nociceptive nerves regulate haematopoietic stem cell mobilization.

Haematopoietic stem cells (HSCs) reside in specialized microenvironments in the bone marrow-often referred to as 'niches'-that represent complex regulatory milieux influenced by multiple cellular constituents, including nerves1,2. Although sympathetic nerves are known to regulate the HSC niche3-6, the contribution of nociceptive neurons in the bone marrow remains unclear. Here we show that nociceptive nerves are required for enforced HSC mobilization and that they collaborate with sympathetic nerves to maintain HSCs in the bone marrow. Nociceptor neurons drive granulocyte colony-stimulating factor (G-CSF)-induced HSC mobilization via the secretion of calcitonin gene-related peptide (CGRP). Unlike sympathetic nerves, which regulate HSCs indirectly via the niche3,4,6, CGRP acts directly on HSCs via receptor activity modifying protein 1 (RAMP1) and the calcitonin receptor-like receptor (CALCRL) to promote egress by activating the Gαs/adenylyl cyclase/cAMP pathway. The ingestion of food containing capsaicin-a natural component of chili peppers that can trigger the activation of nociceptive neurons-significantly enhanced HSC mobilization in mice. Targeting the nociceptive nervous system could therefore represent a strategy to improve the yield of HSCs for stem cell-based therapeutic agents.

Single-particle imaging of nanomedicine entering the brain.

Nanomedicine has emerged as a revolutionary strategy of drug delivery. However, fundamentals of the nano-neuro interaction are elusive. In particular, whether nanocarriers can cross the blood-brain barrier (BBB) and release the drug cargo inside the brain, a basic process depicted in numerous books and reviews, remains controversial. Here, we develop an optical method, based on stimulated Raman scattering, for imaging nanocarriers in tissues. Our method achieves a suite of capabilities-single-particle sensitivity, chemical specificity, and particle counting capability. With this method, we visualize individual intact nanocarriers crossing the BBB of mouse brains and quantify the absolute number by particle counting. The fate of nanocarriers after crossing the BBB shows remarkable heterogeneity across multiple scales. With a mouse model of aging, we find that blood-brain transport of nanocarriers decreases with age substantially. This technology would facilitate development of effective therapeutics for brain diseases and clinical translation of nanocarrier-based treatment in general.

Rapid single-particle chemical imaging of nanoplastics by SRS microscopy.

Plastics are now omnipresent in our daily lives. The existence of microplastics (1 µm to 5 mm in length) and possibly even nanoplastics (<1 µm) has recently raised health concerns. In particular, nanoplastics are believed to be more toxic since their smaller size renders them much more amenable, compared to microplastics, to enter the human body. However, detecting nanoplastics imposes tremendous analytical challenges on both the nano-level sensitivity and the plastic-identifying specificity, leading to a knowledge gap in this mysterious nanoworld surrounding us. To address these challenges, we developed a hyperspectral stimulated Raman scattering (SRS) imaging platform with an automated plastic identification algorithm that allows micro-nano plastic analysis at the single-particle level with high chemical specificity and throughput. We first validated the sensitivity enhancement of the narrow band of SRS to enable high-speed single nanoplastic detection below 100 nm. We then devised a data-driven spectral matching algorithm to address spectral identification challenges imposed by sensitive narrow-band hyperspectral imaging and achieve robust determination of common plastic polymers. With the established technique, we studied the micro-nano plastics from bottled water as a model system. We successfully detected and identified nanoplastics from major plastic types. Micro-nano plastics concentrations were estimated to be about 2.4 ± 1.3 × 105 particles per liter of bottled water, about 90% of which are nanoplastics. This is orders of magnitude more than the microplastic abundance reported previously in bottled water. High-throughput single-particle counting revealed extraordinary particle heterogeneity and nonorthogonality between plastic composition and morphologies; the resulting multidimensional profiling sheds light on the science of nanoplastics.

Connection between protein-tyrosine kinase inhibition and coping with oxidative stress in Bacillus subtilis.

In bacteria, attenuation of protein-tyrosine phosphorylation occurs during oxidative stress. The main described mechanism behind this effect is the H2O2-triggered conversion of bacterial phospho-tyrosines to protein-bound 3,4-dihydroxyphenylalanine. This disrupts the bacterial tyrosine phosphorylation-based signaling network, which alters the bacterial polysaccharide biosynthesis. Herein, we report an alternative mechanism, in which oxidative stress leads to a direct inhibition of bacterial protein-tyrosine kinases (BY-kinases). We show that DefA, a minor peptide deformylase, inhibits the activity of BY-kinase PtkA when Bacillus subtilis is exposed to oxidative stress. High levels of PtkA activity are known to destabilize B. subtilis pellicle formation, which leads to higher sensitivity to oxidative stress. Interaction with DefA inhibits both PtkA autophosphorylation and phosphorylation of its substrate Ugd, which is involved in exopolysaccharide formation. Inactivation of defA drastically reduces the capacity of B. subtilis to cope with oxidative stress, but it does not affect the major oxidative stress regulons PerR, OhrR, and Spx, indicating that PtkA inhibition is the main pathway for DefA involvement in this stress response. Structural analysis identified DefA residues Asn95, Tyr150, and Glu152 as essential for interaction with PtkA. Inhibition of PtkA depends also on the presence of a C-terminal α-helix of DefA, which resembles PtkA-interacting motifs from known PtkA activators, TkmA, SalA, and MinD. Loss of either the key interacting residues or the inhibitory helix of DefA abolishes inhibition of PtkA in vitro and impairs postoxidative stress recovery in vivo, confirming the involvement of these structural features in the proposed mechanism.

CD44 splice isoform switching determines breast cancer stem cell state.

Although changes in alternative splicing have been observed in cancer, their functional contributions still remain largely unclear. Here we report that splice isoforms of the cancer stem cell (CSC) marker CD44 exhibit strikingly opposite functions in breast cancer. Bioinformatic annotation in patient breast cancer in The Cancer Genome Atlas (TCGA) database reveals that the CD44 standard splice isoform (CD44s) positively associates with the CSC gene signatures, whereas the CD44 variant splice isoforms (CD44v) exhibit an inverse association. We show that CD44s is the predominant isoform expressed in breast CSCs. Elimination of the CD44s isoform impairs CSC traits. Conversely, manipulating the splicing regulator ESRP1 to shift alternative splicing from CD44v to CD44s leads to an induction of CSC properties. We further demonstrate that CD44s activates the PDGFRß/Stat3 cascade to promote CSC traits. These results reveal CD44 isoform specificity in CSC and non-CSC states and suggest that alternative splicing provides functional gene versatility that is essential for distinct cancer cell states and thus cancer phenotypes.

Accurate transcriptome-wide identification and quantification of alternative polyadenylation from RNA-seq data with APAIQ.

Alternative polyadenylation (APA) enables a gene to generate multiple transcripts with different 3' ends, which is dynamic across different cell types or conditions. Many computational methods have been developed to characterize sample-specific APA using the corresponding RNA-seq data, but suffered from high error rate on both polyadenylation site (PAS) identification and quantification of PAS usage (PAU), and bias toward 3' untranslated regions. Here we developed a tool for APA identification and quantification (APAIQ) from RNA-seq data, which can accurately identify PAS and quantify PAU in a transcriptome-wide manner. Using 3' end-seq data as the benchmark, we showed that APAIQ outperforms current methods on PAS identification and PAU quantification, including DaPars2, Aptardi, mountainClimber, SANPolyA, and QAPA. Finally, applying APAIQ on 421 RNA-seq samples from liver cancer patients, we identified >540 tumor-associated APA events and experimentally validated two intronic polyadenylation candidates, demonstrating its capacity to unveil cancer-related APA with a large-scale RNA-seq data set.

Uniform thin ice on ultraflat graphene for high-resolution cryo-EM.

Cryo-electron microscopy (cryo-EM) visualizes the atomic structure of macromolecules that are embedded in vitrified thin ice at their close-to-native state. However, the homogeneity of ice thickness, a key factor to ensure high image quality, is poorly controlled during specimen preparation and has become one of the main challenges for high-resolution cryo-EM. Here we found that the uniformity of thin ice relies on the surface flatness of the supporting film, and developed a method to use ultraflat graphene (UFG) as the support for cryo-EM specimen preparation to achieve better control of vitreous ice thickness. We show that the uniform thin ice on UFG improves the image quality of vitrified specimens. Using such a method we successfully determined the three-dimensional structures of hemoglobin (64 kDa), α-fetoprotein (67 kDa) with no symmetry, and streptavidin (52 kDa) at a resolution of 3.5 Å, 2.6 Å and 2.2 Å, respectively. Furthermore, our results demonstrate the potential of UFG for the fields of cryo-electron tomography and structure-based drug discovery.

AI identifies potent inducers of breast cancer stem cell differentiation based on adversarial learning from gene expression data.

Cancer stem cells (CSCs) are a subpopulation of cancer cells within tumors that exhibit stem-like properties and represent a potentially effective therapeutic target toward long-term remission by means of differentiation induction. By leveraging an artificial intelligence approach solely based on transcriptomics data, this study scored a large library of small molecules based on their predicted ability to induce differentiation in stem-like cells. In particular, a deep neural network model was trained using publicly available single-cell RNA-Seq data obtained from untreated human-induced pluripotent stem cells at various differentiation stages and subsequently utilized to screen drug-induced gene expression profiles from the Library of Integrated Network-based Cellular Signatures (LINCS) database. The challenge of adapting such different data domains was tackled by devising an adversarial learning approach that was able to effectively identify and remove domain-specific bias during the training phase. Experimental validation in MDA-MB-231 and MCF7 cells demonstrated the efficacy of five out of six tested molecules among those scored highest by the model. In particular, the efficacy of triptolide, OTS-167, quinacrine, granisetron and A-443654 offer a potential avenue for targeted therapies against breast CSCs.

HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses.

While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen presentation by human leukocyte antigen (HLA) class II molecules remains a challenge. This identification is critical for developing vaccines and cancer immunotherapies. Current prediction methods are limited, primarily due to a lack of high-quality training epitope datasets and algorithmic constraints. To predict the exogenous HLA class II-restricted peptides across most of the human population, we utilized the mass spectrometry data to profile >223 000 eluted ligands over HLA-DR, -DQ, and -DP alleles. Here, by integrating these data with peptide processing and gene expression, we introduce HLAIImaster, an attention-based deep learning framework with adaptive domain knowledge for predicting neoepitope immunogenicity. Leveraging diverse biological characteristics and our enhanced deep learning framework, HLAIImaster is significantly improved against existing tools in terms of positive predictive value across various neoantigen studies. Robust domain knowledge learning accurately identifies neoepitope immunogenicity, bridging the gap between neoantigen biology and the clinical setting and paving the way for future neoantigen-based therapies to provide greater clinical benefit. In summary, we present a comprehensive exploitation of the immunogenic neoepitope repertoire of cancers, facilitating the effective development of "just-in-time" personalized vaccines.

Microwave Encounters Ionic Liquid: Synergistic Mechanism, Synthesis and Emerging Applications.

Progress in microwave (MW) energy application technology has stimulated remarkable advances in manufacturing and high-quality applications of ionic liquids (ILs) that are generally used as novel media in chemical engineering. This Review focuses on an emerging technology via the combination of MW energy and the usage of ILs, termed microwave-assisted ionic liquid (MAIL) technology. In comparison to conventional routes that rely on heat transfer through media, the contactless and unique MW heating exploits the electromagnetic wave-ions interactions to deliver energy to IL molecules, accelerating the process of material synthesis, catalytic reactions, and so on. In addition to the inherent advantages of ILs, including outstanding solubility, and well-tuned thermophysical properties, MAIL technology has exhibited great potential in process intensification to meet the requirement of efficient, economic chemical production. Here we start with an introduction to principles of MW heating, highlighting fundamental mechanisms of MW induced process intensification based on ILs. Next, the synergies of MW energy and ILs employed in materials synthesis, as well as their merits, are documented. The emerging applications of MAIL technologies are summarized in the next sections, involving tumor therapy, organic catalysis, separations, and bioconversions. Finally, the current challenges and future opportunities of this emerging technology are discussed.

Colorful low-emissivity paints for space heating and cooling energy savings.

Space heating and cooling consume ~13% of global energy every year. The development of advanced materials that promote energy savings in heating and cooling is gaining increasing attention. To thermally isolate the space of concern and minimize the heat exchange with the outside environment has been recognized as one effective solution. To this end, here, we develop a universal category of colorful low-emissivity paints to form bilayer coatings consisting of an infrared (IR)-reflective bottom layer and an IR-transparent top layer in colors. The colorful visual appearance ensures the aesthetical effect comparable to conventional paints. High mid-infrared reflectance (up to ~80%) is achieved, which is more than 10 times as conventional paints in the same colors, efficiently reducing both heat gain and loss from/to the outside environment. The high near-IR reflectance also benefits reducing solar heat gain in hot days. The advantageous features of these paints strike a balance between energy savings and penalties for heating and cooling throughout the year, providing a comprehensive year-round energy-saving solution adaptable to a wide variety of climatic zones. Taking a typical midrise apartment building as an example, the application of our colorful low-emissivity paints can realize positive heating, ventilation, and air conditioning energy saving, up to 27.24 MJ/m2/y (corresponding to the 7.4% saving ratio). Moreover, the versatility of the paint, along with its applicability to diverse surfaces of various shapes and materials, makes the paints extensively useful in a range of scenarios, including building envelopes, transportation, and storage.

Data-driven electrolyte design for lithium metal anodes.

Improving Coulombic efficiency (CE) is key to the adoption of high energy density lithium metal batteries. Liquid electrolyte engineering has emerged as a promising strategy for improving the CE of lithium metal batteries, but its complexity renders the performance prediction and design of electrolytes challenging. Here, we develop machine learning (ML) models that assist and accelerate the design of high-performance electrolytes. Using the elemental composition of electrolytes as the features of our models, we apply linear regression, random forest, and bagging models to identify the critical features for predicting CE. Our models reveal that a reduction in the solvent oxygen content is critical for superior CE. We use the ML models to design electrolyte formulations with fluorine-free solvents that achieve a high CE of 99.70%. This work highlights the promise of data-driven approaches that can accelerate the design of high-performance electrolytes for lithium metal batteries.

Electrolytes with moderate lithium polysulfide solubility for high-performance long-calendar-life lithium-sulfur batteries.

Lithium-sulfur (Li-S) batteries with high energy density and low cost are promising for next-generation energy storage. However, their cycling stability is plagued by the high solubility of lithium polysulfide (LiPS) intermediates, causing fast capacity decay and severe self-discharge. Exploring electrolytes with low LiPS solubility has shown promising results toward addressing these challenges. However, here, we report that electrolytes with moderate LiPS solubility are more effective for simultaneously limiting the shuttling effect and achieving good Li-S reaction kinetics. We explored a range of solubility from 37 to 1,100 mM (based on S atom, [S]) and found that a moderate solubility from 50 to 200 mM [S] performed the best. Using a series of electrolyte solvents with various degrees of fluorination, we formulated the Single-Solvent, Single-Salt, Standard Salt concentration with Moderate LiPSs solubility Electrolytes (termed S6MILE) for Li-S batteries. Among the designed electrolytes, Li-S cells using fluorinated-1,2-diethoxyethane S6MILE (F4DEE-S6MILE) showed the highest capacity of 1,160 mAh g-1 at 0.05 C at room temperature. At 60 °C, fluorinated-1,4-dimethoxybutane S6MILE (F4DMB-S6MILE) gave the highest capacity of 1,526 mAh g-1 at 0.05 C and an average CE of 99.89% for 150 cycles at 0.2 C under lean electrolyte conditions. This is a fivefold increase in cycle life compared with other conventional ether-based electrolytes. Moreover, we observed a long calendar aging life, with a capacity increase/recovery of 4.3% after resting for 30 d using F4DMB-S6MILE. Furthermore, the correlation between LiPS solubility, degree of fluorination of the electrolyte solvent, and battery performance was systematically investigated.

Correlating chemistry and mass transport in sustainable iron production.

Steelmaking contributes 8% to the total CO2 emissions globally, primarily due to coal-based iron ore reduction. Clean hydrogen-based ironmaking has variable performance because the dominant gas-solid reduction mechanism is set by the defects and pores inside the mm- to nm-sized oxide particles that change significantly as the reaction progresses. While these governing dynamics are essential to establish continuous flow of iron and its ores through reactors, the direct link between agglomeration and chemistry is still contested due to missing measurements. In this work, we directly measure the connection between chemistry and agglomeration in the smallest iron oxides relevant to magnetite ores. Using synthesized spherical 10-nm magnetite particles reacting in H2, we resolve the formation and consumption of wüstite (Fe1-xO)-the step most commonly attributed to whiskering. Using X-ray diffraction, we resolve crystallographic anisotropy in the rate of the initial reaction. Complementary imaging demonstrated how the particles self-assemble, subsequently react, and grow into elongated "whisker" structures. Our insights into how morphologically uniform iron oxide particles react and agglomerate in H2 reduction enable future size-dependent models to effectively describe the multiscale aspects of iron ore reduction.