Multimodal charting of molecular and functional cell states via in situ electro-sequencing.

Paired mapping of single-cell gene expression and electrophysiology is essential to understand gene-to-function relationships in electrogenic tissues. Here, we developed in situ electro-sequencing (electro-seq) that combines flexible bioelectronics with in situ RNA sequencing to stably map millisecond-timescale electrical activity and profile single-cell gene expression from the same cells across intact biological networks, including cardiac and neural patches. When applied to human-induced pluripotent stem-cell-derived cardiomyocyte patches, in situ electro-seq enabled multimodal in situ analysis of cardiomyocyte electrophysiology and gene expression at the cellular level, jointly defining cell states and developmental trajectories. Using machine-learning-based cross-modal analysis, in situ electro-seq identified gene-to-electrophysiology relationships throughout cardiomyocyte development and accurately reconstructed the evolution of gene expression profiles based on long-term stable electrical measurements. In situ electro-seq could be applicable to create spatiotemporal multimodal maps in electrogenic tissues, potentiating the discovery of cell types and gene programs responsible for electrophysiological function and dysfunction.

Spatiotemporally resolved transcriptomics reveals the subcellular RNA kinetic landscape.

Spatiotemporal regulation of the cellular transcriptome is crucial for proper protein expression and cellular function. However, the intricate subcellular dynamics of RNA remain obscured due to the limitations of existing transcriptomics methods. Here, we report TEMPOmap-a method that uncovers subcellular RNA profiles across time and space at the single-cell level. TEMPOmap integrates pulse-chase metabolic labeling with highly multiplexed three-dimensional in situ sequencing to simultaneously profile the age and location of individual RNA molecules. Using TEMPOmap, we constructed the subcellular RNA kinetic landscape in various human cells from transcription and translocation to degradation. Clustering analysis of RNA kinetic parameters across single cells revealed 'kinetic gene clusters' whose expression patterns were shaped by multistep kinetic sculpting. Importantly, these kinetic gene clusters are functionally segregated, suggesting that subcellular RNA kinetics are differentially regulated in a cell-state- and cell-type-dependent manner. Spatiotemporally resolved transcriptomics provides a gateway to uncovering new spatiotemporal gene regulation principles.

AI-guided histopathology predicts brain metastasis in lung cancer patients.

Brain metastases can occur in nearly half of patients with early and locally advanced (stage I-III) non-small cell lung cancer (NSCLC). There are no reliable histopathologic or molecular means to identify those who are likely to develop brain metastases. We sought to determine if deep learning (DL) could be applied to routine H&E-stained primary tumor tissue sections from stage I-III NSCLC patients to predict the development of brain metastasis. Diagnostic slides from 158 patients with stage I-III NSCLC followed for at least 5 years for the development of brain metastases (Met+, 65 patients) versus no progression (Met-, 93 patients) were subjected to whole-slide imaging. Three separate iterations were performed by first selecting 118 cases (45 Met+, 73 Met-) to train and validate the DL algorithm, while 40 separate cases (20 Met+, 20 Met-) were used as the test set. The DL algorithm results were compared to a blinded review by four expert pathologists. The DL-based algorithm was able to distinguish the eventual development of brain metastases with an accuracy of 87% (p < 0.0001) compared with an average of 57.3% by the four pathologists and appears to be particularly useful in predicting brain metastases in stage I patients. The DL algorithm appears to focus on a complex set of histologic features. DL-based algorithms using routine H&E-stained slides may identify patients who are likely to develop brain metastases from those who will remain disease free over extended (>5 year) follow-up and may thus be spared systemic therapy. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Resonant cold scattering of highly vibrationally excited D2 with Ne.

To accurately map weak D2-Ne long-range interactions, we have studied rotationally inelastic cold scattering of D2 prepared in the vibrationally excited (v = 4) and rotationally aligned (j = 2, m) quantum state within the moving frame of a supersonically expanded mixed molecular beam. In contrast to earlier high energy D2-Ne collision experiments, the (j = 2 → j' = 0) cold scattering produced highly symmetric angular distributions that strongly suggest a resonant quasi-bound collision complex that lives long enough to make a few rotations. Our partial wave analysis indicates that the scattering dynamics is dominated by a single resonant l = 2 orbital, even in the presence of a broad temperature (0-5 K) distribution that allows incoming orbitals up to l = 5. The dominance of a single orbital suggests that the resonant complex stabilizes through the coupling of the internal (j = 2) and orbital (l = 2) angular momentum to produce a total angular momentum of J = 0 for the D2-Ne complex.

Non-recursive transport of intensity phase retrieval with the transport of phase.

The transport of intensity equation (TIE) is a non-interferometric phase retrieval method that originates from the imaginary part of the Helmholtz equation and is equivalent to the law of conservation of energy. From the real part of the Helmholtz equation, the transport of phase equation (TPE), which represents the Eikonal equation in the presence of diffraction, can be derived. The amplitude and phase for an arbitrary optical field should satisfy these coupled equations simultaneously during propagation. In this work, the coupling between the TIE and TPE is exploited to improve the phase retrieval solutions from the TIE. Specifically, a non-recursive fast Fourier transform (FFT)-based phase retrieval method using both the TIE and TPE is demonstrated. Based on the FFT-based TIE solution, a correction factor calculated by the TPE is introduced to improve the phase retrieval results.

Use of structured light in 3D reconstruction of transparent objects.

A simple non-interferometric incoherent light ray propagation model is introduced to perform three-dimensional profiling of transparent objects with typical thicknesses of the order of mm to cm by analyzing the distorted captured image behind the object. A two-dimensional cosine fringe is used as the incident reference image, whose periodicity is markedly altered by the shape of the object. By monitoring the local change in the period, the surface profile is simulated and optimized to achieve minimal error with experimental data and thus determine the final morphology. Our proposed method is simple, robust, straightforward, and single-shot, and can be used with coherent or incoherent illumination. Its feasibility for more complex applications is verified experimentally through rigorous error calculation. Moreover, the application of this technique for arbitrary transparent objects is theoretically attainable and promising.

Shape resonance determined from angular distribution in D2 (v = 2, j = 2) + He → D2 (v = 2, j = 0) + He cold scattering.

We find an l = 2 shape resonance fingerprinted in the angular distribution of the cold (∼1 K) Δj = 2 rotationally inelastic collision of D2 with He in a single supersonic expansion. The Stark-induced adiabatic Raman passage is used to prepare D2 in the (v = 2, j = 2) rovibrational level with control of the spatial distribution of the bond axis of the molecule by magnetic sublevel selection. We show that the rate of Δj = 2 D2-D2 relaxation is nearly two orders of magnitude weaker than that of D2-He. This suggests that the strong D2-He scattering is caused by an orbiting resonance that is highly sensitive to the shape of the long-range potential.

Single-shot digital phase-shifting Moiré patterns for 3D topography.

A simple and robust technique of Moiré topography with single-image capture and incorporating digital filtering along with a four-step digitally implemented phase-shifting method is introduced for three-dimensional (3D) surface mapping. Feature details in the order of tens to hundreds of microns can be achieved using interferometrically generated structured light to illuminate the object surface. Compared to the traditional optical phase-shifting method, a digital phase-shifting method based on Fourier processing is implemented with computer-generated sinusoidal patterns derived from the recorded deformed fringes. This enables a single capture of the image that can be used to reconstruct the 3D topography of the surface. Single-shot imaging is simple to implement experimentally and avoids errors in introducing the correct phase shifts. The feasibility of this technique is verified experimentally, and applications to metallic surfaces are demonstrated.

Performance analysis of phase retrieval using transport of intensity with digital holography [Invited].

The performance of direct and unwrapped phase retrieval, which combines digital holography with the transport of intensity, is examined in detail in this paper. In this technique, digital holography is used to numerically reconstruct the intensities at different planes around the image plane, and phase retrieval is achieved by the transport of intensity. Digital holography with transport of intensity is examined for inline and off-axis geometries. The effect of twin images in the inline case is evaluated. Phase-shifting digital holography with transport of intensity is introduced. The performance of digital holography with transport of intensity is compared with traditional off-axis single- and dual-wavelength techniques, which employ standard phase unwrapping algorithms. Simulations and experiments are performed to determine and compare the accuracy of phase retrieval through a mean-squared-error figure of merit as well as the computational speeds of the various methods.

Harnessing the Power of Adiabatic Curve Crossing to Populate the Highly Vibrationally Excited H_{2} (v=7, j=0) Level.

A large ensemble of ∼10^{9} H_{2} (v=7, j=0) molecules is prepared in the collision-free environment of a supersonic beam by transferring nearly the entire H_{2} (v=0, j=0) ground-state population, where v and j are the vibrational and rotational quantum numbers, respectively. This is accomplished by controlling the crossing of the optically dressed adiabatic states using a pair of phase coherent laser pulses. The preparation of highly vibrationally excited H_{2} molecules opens new opportunities to test fundamental physical principles using two loosely bound yet entangled H atoms.

3D object recognition through processing of 2D holograms.

Correlation of two-dimensional digitally recorded holograms is introduced as a novel approach for object recognition without the need for quantitative assessment of the retrieved complex field, based on the fact that a hologram contains the three-dimensional information of the object. Actual objects with different three-dimensional features such as depth and surface roughness are assessed through processing of the correlation of their two-dimensional holograms. Correlation peak values are extracted as a metric to evaluate correlation of three-dimensional objects. The effect of hologram windowing size on correlation of three-dimensional objects is investigated, and improvements in computation time and dynamic range are assessed. Critical figures of merit used for assessment of correlation of images are applied to the correlation of holograms for object recognition.

Digital correlation of computer-generated holograms for 3D face recognition.

Three-dimensional (3D) face recognition has been a crucial task in human biometric verification and identification. A digital correlation method of a computer-generated hologram (CGH) for 3D face recognition is proposed, which encodes 3D data into a 2D hologram for recognition. The 3D face models are preprocessed and compressed to into groups of feature points. The CGH templates corresponding to the 3D feature points are generated by point- and layer-oriented algorithms based on three different numerical algorithms to encode depth values into 2D holograms. A 2D digital correlation is performed between the CGH templates. It is demonstrated that the generated CGHs templates could be effectively classified based on the correlation performance metrics of discrimination ratio, peak-to-correlation plane energy, and peak-to-noise ratio. With the essence of the CGH algorithm being the conversion of 3D data to a 2D hologram, the proposed encoding and decoding method has great advantages in reducing computational efforts and potential applications in 3D face recognition, storage, and display.

Adeno-Associated Virus-Mediated Mini-Agrin Delivery Is Unable to Rescue Disease Phenotype in a Mouse Model of Limb Girdle Muscular Dystrophy Type 2I.

Agrin is a basement membrane-specific proteoglycan that can regulate orientation of cytoskeleton proteins and improve function of dystrophic skeletal muscle. In skeletal muscle, agrin binds with high affinity to laminin(s) and α-dystroglycan (α-DG), an integral part of the dystrophin-glycoprotein complex. Miniaturized forms of agrin (mAgrin) have been shown to ameliorate disease pathology in a laminin-α2 knockout mouse model of muscular dystrophy, acting as a link between α-DG and laminin(s). Here, we test whether mAgrin might also improve pathologies associated with FKRP-related dystroglycanopathies, another form of muscular dystrophy characterized by weak interactions between muscle and basement membranes. We demonstrate in vitro that mAgrin enhances laminin binding to primary myoblasts and fibroblasts from an FKRP mutant mouse model and that this enhancement is abrogated when mAgrin is in molar excess relative to laminin. However, in vivo delivery of mAgrin via adeno-associated virus (AAV) into FKRP mutant mice was unable to improve dystrophic phenotypes, both histologically and functionally. These results likely reflect insufficient binding of mAgrin to hypoglycosylated α-DG on muscle fibers and possibly abrogation of binding from molar excess of overexpressed AAV-delivered mAgrin. Further exploration of mAgrin modification is necessary to strengthen its binding to other membrane components, including hypoglycosylated α-DG, for potential therapeutic applications.

Efficient, gigapixel-scale, aberration-free whole slide scanner using angular ptychographic imaging with closed-form solution.

Whole slide imaging provides a wide field-of-view (FOV) across cross-sections of biopsy or surgery samples, significantly facilitating pathological analysis and clinical diagnosis. Such high-quality images that enable detailed visualization of cellular and tissue structures are essential for effective patient care and treatment planning. To obtain such high-quality images for pathology applications, there is a need for scanners with high spatial bandwidth products, free from aberrations, and without the requirement for z-scanning. Here we report a whole slide imaging system based on angular ptychographic imaging with a closed-form solution (WSI-APIC), which offers efficient, tens-of-gigapixels, large-FOV, aberration-free imaging. WSI-APIC utilizes oblique incoherent illumination for initial high-level segmentation, thereby bypassing unnecessary scanning of the background regions and enhancing image acquisition efficiency. A GPU-accelerated APIC algorithm analytically reconstructs phase images with effective digital aberration corrections and improved optical resolutions. Moreover, an auto-stitching technique based on scale-invariant feature transform ensures the seamless concatenation of whole slide phase images. In our experiment, WSI-APIC achieved an optical resolution of 772 nm using a 10×/0.25 NA objective lens and captures 80-gigapixel aberration-free phase images for a standard 76.2 mm × 25.4 mm microscopic slide.

Impact of Stain Variation and Color Normalization for Prognostic Predictions in Pathology.

In recent years, deep neural networks (DNNs) have demonstrated remarkable performance in pathology applications, potentially even outperforming expert pathologists due to their ability to learn subtle features from large datasets. One complication in preparing digital pathology datasets for DNN tasks is variation in tinctorial qualities. A common way to address this is to perform stain normalization on the images. In this study, we show that a well-trained DNN model trained on one batch of histological slides failed to generalize to another batch prepared at a different time from the same tissue blocks, even when stain normalization methods were applied. This study used sample data from a previously reported DNN that was able to identify patients with early stage non-small cell lung cancer (NSCLC) whose tumors did and did not metastasize, with high accuracy, based on training and then testing of digital images from H&E stained primary tumor tissue sections processed at the same time. In this study we obtained a new series of histologic slides from the adjacent recuts of same tissue blocks processed in the same lab but at a different time. We found that the DNN trained on the either batch of slides/images was unable to generalize and failed to predict progression in the other batch of slides/images (AUCcross-batch = 0.52 - 0.53 compared to AUCsame-batch = 0.74 - 0.81). The failure to generalize did not improve even when the tinctorial difference correction were made through either traditional color-tuning or stain normalization with the help of a Cycle Generative Adversarial Network (CycleGAN) process. This highlights the need to develop an entirely new way to process and collect consistent microscopy images from histologic slides that can be used to both train and allow for the general application of predictive DNN algorithms.

A Deep Learning Model for Predicting Molecular Subtype of Breast Cancer by Fusing Multiple Sequences of DCE-MRI From Two Institutes.

RATIONALE AND OBJECTIVES: To evaluate the performance of deep learning (DL) in predicting different breast cancer molecular subtypes using DCE-MRI from two institutes. MATERIALS AND METHODS: This retrospective study included 366 breast cancer patients from two institutes, divided into training (n = 292), validation (n = 49) and testing (n = 25) sets. We first transformed the public DCE-MRI appearance to ours to alleviate small-data-size and class-imbalance issues. Second, we developed a multi-branch convolutional-neural-network (MBCNN) to perform molecular subtype prediction. Third, we assessed the MBCNN with different regions of interest (ROIs) and fusion strategies, and compared it to previous DL models. Area under the curve (AUC) and accuracy (ACC) were used to assess different models. Delong-test was used for the comparison of different groups. RESULTS: MBCNN achieved the optimal performance under intermediate fusion and ROI size of 80 pixels with appearance transformation. It outperformed CNN and convolutional long-short-term-memory (CLSTM) in predicting luminal B, HER2-enriched and TN subtypes, but without demonstrating statistical significance except against CNN in TN subtypes, with testing AUCs of 0.8182 vs. [0.7208, 0.7922] (p=0.44, 0.80), 0.8500 vs. [0.7300, 0.8200] (p=0.36, 0.70) and 0.8900 vs. [0.7600, 0.8300] (p=0.03, 0.63), respectively. When predicting luminal A, MBCNN outperformed CNN with AUCs of 0.8571 vs. 0.7619 (p=0.08) without achieving statistical significance, and is comparable to CLSTM. For four-subtype prediction, MBCNN achieved an ACC of 0.64, better than CNN and CLSTM models with ACCs of 0.48 and 0.52, respectively. CONCLUSION: Developed DL model with the feature extraction and fusion of DCE-MRI from two institutes enabled preoperative prediction of breast cancer molecular subtypes with high diagnostic performance.

Length-scale study in deep learning prediction for non-small cell lung cancer brain metastasis.

Deep learning-assisted digital pathology has demonstrated the potential to profoundly impact clinical practice, even surpassing human pathologists in performance. However, as deep neural network (DNN) architectures grow in size and complexity, their explainability decreases, posing challenges in interpreting pathology features for broader clinical insights into physiological diseases. To better assess the interpretability of digital microscopic images and guide future microscopic system design, we developed a novel method to study the predictive feature length-scale that underpins a DNN's predictive power. We applied this method to analyze a DNN's capability in predicting brain metastasis from early-stage non-small-cell lung cancer biopsy slides. This study quantifies DNN's attention for brain metastasis prediction, targeting features at both the cellular scale and tissue scale in H&E-stained histological whole slide images. At the cellular scale, the predictive power of DNNs progressively increases with higher resolution and significantly decreases when the resolvable feature length exceeds 5 microns. Additionally, DNN uses more macro-scale features associated with tissue architecture and is optimized when assessing visual fields greater than 41 microns. Our study computes the length-scale requirements for optimal DNN learning on digital whole-slide microscopic images, holding the promise to guide future optical microscope designs in pathology applications and facilitating downstream deep learning analysis.

ZBTB11 Depletion Targets Metabolic Vulnerabilities in K-Ras Inhibitor Resistant PDAC.

Over 95% of pancreatic ductal adenocarcinomas (PDAC) harbor oncogenic mutations in K-Ras. Upon treatment with K-Ras inhibitors, PDAC cancer cells undergo metabolic reprogramming towards an oxidative phosphorylation-dependent, drug-resistant state. However, direct inhibition of complex I is poorly tolerated in patients due to on-target induction of peripheral neuropathy. In this work, we develop molecular glue degraders against ZBTB11, a C2H2 zinc finger transcription factor that regulates the nuclear transcription of components of the mitoribosome and electron transport chain. Our ZBTB11 degraders leverage the differences in demand for biogenesis of mitochondrial components between human neurons and rapidly-dividing pancreatic cancer cells, to selectively target the K-Ras inhibitor resistant state in PDAC. Combination treatment of both K-Ras inhibitor-resistant cell lines and multidrug resistant patient-derived organoids resulted in superior anti-cancer activity compared to single agent treatment, while sparing hiPSC-derived neurons. Proteomic and stable isotope tracing studies revealed mitoribosome depletion and impairment of the TCA cycle as key events that mediate this response. Together, this work validates ZBTB11 as a vulnerability in K-Ras inhibitor-resistant PDAC and provides a suite of molecular glue degrader tool compounds to investigate its function.

Assaying Chemical Long-Term Potentiation in Human iPSC-Derived Neuronal Networks.

Impairment of long-term potentiation (LTP) is a common feature of many preclinical models of neurological disorders. Modeling LTP on human induced pluripotent stem cells (hiPSC) enables the investigation of this critical plasticity process in disease-specific genetic backgrounds. Here, we describe a method to chemically induce LTP across entire networks of hiPSC-derived neurons on multi-electrode arrays (MEAs) and investigate effects on neuronal network activity and associated molecular changes.

CO Diffusion Study and Spatial and Temporal Variation Modeling during the Construction Period of the Plateau Railroad Tunnel.

In order to investigate the diffusion law of CO gas in the vicinity of the tunnel boring face of the plateau long tunnel, to improve the efficiency of tunnel smoke exhaust, and to derive the spatial-temporal variation model of CO concentration for predicting the concentration of CO at different times and in different cross sections under specific environments, a CO diffusion model of a tunnel in Yunnan was established by using Ansys Fluent Fluid Simulation Software, and the CO transport characteristics under different conditions were simulated by taking the ventilation time, wind speed, and location of the air ducts as the influencing factors. The results show that the wind flows from the mouth of the wind pipe after the wind speed decreases, the diffusion area increases and arrives at the face of the direction of the rebound in the jet stream of new wind, and the return wind under the joint action of the vortex produced obviously, to reach the wind pipe mouth after the tunnel wind flow field, basically tends to stabilize. When the wind pipe mouth was arranged in the arch waist, 20 m away from the boring face, the inlet wind speed was 9 m/s and the ventilation time was 30 min; the CO concentration in the tunnel was reduced to below the maximum allowable concentration value. Moreover, the concentration of CO in the tunnel at the moment of 15 min of ventilation has a nonlinear positive correlation with the change of distance L from the boring face, while at the cross section of the air outlet of the wind pipe L = 20 m, the ventilation time is from 1 to 30 min and the concentration of CO at the cross section has a nonlinear decreasing trend with the ventilation time, which can be deduced according to the different space-time change models.