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Single particle tracking is an essential tool in the study of complex systems and biophysics and it is commonly analyzed by the time-averaged mean square displacement (MSD) of the diffusive trajectories. However, past work has shown that MSDs are susceptible to significant errors and biases, preventing the comparison and assessment of experimental studies. Here, we attempt to extract practical guidelines for the estimation of anomalous time averaged MSDs through the simulation of multiple scenarios with fractional Brownian motion as a representative of a large class of fractional ergodic processes. We extract the precision and accuracy of the fitted MSD for various anomalous exponents and measurement errors with respect to measurement length and maximum time lags. Based on the calculated precision maps, we present guidelines to improve accuracy in single particle studies. Importantly, we find that in some experimental conditions, the time averaged MSD should not be used as an estimator.

Thyroid cancer is the most prevalent endocrine cancer, with a steadily rising incidence. Its diagnosis involves microscopic examination of tissue specimens, a process that can lead to misclassification with critical prognostic consequences. Numerous artificial intelligence techniques have been proposed for thyroid cancer detection, however, none have proven clinically relevant. We present an accurate diagnostic approach based on a newly developed spectral imaging system that rapidly measures the visible spectrum at each point on routinely prepared hematoxylin and eosin-stained tissue sections. These spectral images are analyzed with machine learning algorithms, classifying each nucleus while preserving interpretability for experts. The integration of spectral imaging and artificial intelligence enables precise, robust identification of normal and tumor cells, offering a straightforward, powerful approach for thyroid cancer assessment. By utilizing routinely stained tissue specimens and targeting specific pathological features, our method provides a tool to support pathologists, facilitating accurate and timely evaluations of thyroid cancer.

Pathological analysis of tissue biopsies remains the gold standard for diagnosing cancer and other diseases. However, this is a time-intensive process that demands extensive training and expertise. Despite its importance, it is often subjective and not entirely error-free. Over the past decade, pathology has undergone two major transformations. First, the rise in whole slide imaging has enabled work in front of a computer screen and the integration of image processing tools to enhance diagnostics. Second, the rapid evolution of Artificial Intelligence has revolutionized numerous fields and has had a remarkable impact on humanity. The synergy of these two has paved the way for groundbreaking research aiming for advancements in digital pathology. Despite encouraging research outcomes, AI-based tools have yet to be actively incorporated into therapeutic protocols. This is primary due to the need for high reliability in medical therapy, necessitating a new approach that ensures greater robustness. Another approach for improving pathological diagnosis involves advanced optical methods such as spectral imaging, which reveals information from the tissue that is beyond human vision. We have recently developed a unique rapid spectral imaging system capable of scanning pathological slides, delivering a wealth of critical diagnostic information. Here, we present a novel application of spectral imaging (SI) for virtual Hematoxylin and Eosin (H&E) staining using a custom-built, rapid Fourier-based SI system. Unstained human biopsy samples are scanned, and a Pix2Pix-based neural network generates realistic H&E-equivalent images. Additionally, we applied Principal Component Analysis (PCA) to the spectral information to examine the effect of down sampling the data on the virtual staining process. To assess model performance, we trained and tested models using full spectral data, RGB, and PCA-reduced spectral inputs. The results demonstrate that PCA-reduced data preserved essential image features while enhancing statistical image quality, as indicated by FID and KID scores, and reducing computational complexity. These findings highlight the potential of integrating SI and AI to enable efficient, accurate, and stain-free digital pathology.

Lamins form stable filaments at the nuclear periphery in metazoans. Unlike B-type lamins, lamins A and C localize also in the nuclear interior, where they interact with lamin-associated polypeptide 2 alpha (LAP2α). Using antibody labeling, we previously observed a depletion of nucleoplasmic A-type lamins in mouse cells lacking LAP2α. Here, we show that loss of LAP2α actually causes formation of larger, biochemically stable lamin A/C structures in the nuclear interior that are inaccessible to lamin A/C antibodies. While nucleoplasmic lamin A forms from newly expressed pre-lamin A during processing and from soluble mitotic lamins in a LAP2α-independent manner, binding of LAP2α to lamin A/C during interphase inhibits formation of higher order structures, keeping nucleoplasmic lamin A/C in a mobile state independent of lamin A/C S22 phosphorylation. We propose that LAP2α is essential to maintain a mobile lamin A/C pool in the nuclear interior, which is required for proper nuclear functions.

Single integration of a target gene expressing MS2-binding stem loops allows the real time quantification of transcriptional bursts, promoter firings and cell cycle–dependent transcription rates. We generated a system for in vivo visualization and analysis of mammalian mRNA transcriptional kinetics of single alleles in real time, using single-gene integrations. We obtained high-resolution transcription measurements of a single cyclin D1 allele under endogenous or viral promoter control, including quantification of temporal kinetics of transcriptional bursting, promoter firing, nascent mRNA numbers and transcription rates during the cell cycle, and in relation to DNA replication.

Transcription kinetics of actively transcribing genes in vivo have generally been measured using tandem gene arrays. However, tandem arrays do not reflect the endogenous state of genome organization in which genes appear as single alleles. Here we present a robust technique for the quantification of mRNA synthesis from a single allele in real time in single living mammalian cells. The protocol describes how to generate cell clones harboring an MS2-tagged allele and how to detect in vivo transcription from this tagged allele at high spatial and temporal resolution throughout the cell cycle. Quantification of nascent mRNAs produced from the single tagged allele is performed using RNA fluorescence in situ hybridization (FISH) and live-cell imaging. Subsequent analyses and data modeling detailed in the protocol include measurements of transcription rates of RNA polymerase II, determination of the number of polymerases recruited to the tagged allele and measurement of the spacing between polymerases. Generation of the cells containing the single tagged alleles should take up to 1 month; RNA FISH or live-cell imaging will require an additional week.

Varicella Zoster Virus (VZV), the alphaherpesvirus that causes varicella upon primary infection and Herpes zoster (shingles) following reactivation in latently infected neurons, is known to be fusogenic. It forms polynuclear syncytia in culture, in varicella skin lesions and in infected fetal human ganglia xenografted to mice. After axonal infection using VZV expressing green fluorescent protein (GFP) in compartmentalized microfluidic cultures there is diffuse filling of axons with GFP as well as punctate fluorescence corresponding to capsids. Use of viruses with fluorescent fusions to VZV proteins reveals that both proteins encoded by VZV genes and those of the infecting cell are transferred in bulk from infecting non-neuronal cells to axons. Similar transfer of protein to axons was observed following cell associated HSV1 infection. Fluorescence recovery after photobleaching (FRAP) experiments provide evidence that this transfer is by diffusion of proteins from the infecting cells into axons. Time-lapse movies and immunocytochemical experiments in co-cultures demonstrate that non-neuronal cells fuse with neuronal somata and proteins from both cell types are present in the syncytia formed. The fusogenic nature of VZV therefore may enable not only conventional entry of virions and capsids into axonal endings in the skin by classical entry mechanisms, but also by cytoplasmic fusion that permits viral protein transfer to neurons in bulk.

In previous work, we showed that telomeres of normal cells are organized within the 3D space of the interphase nucleus in a nonoverlapping and cell cycle-dependent manner. This order is distorted in tumor cell nuclei where telomeres are found in close association forming aggregates of various numbers and sizes. Here we show that c-Myc overexpression induces telomeric aggregations in the interphase nucleus. Directly proportional to the duration of c-Myc deregulation, we observe three or five cycles of telomeric aggregate formation in interphase nuclei. These cycles reflect the onset and propagation of breakage-bridge-fusion cycles that are initiated by end-to-end telomeric fusions of chromosomes. Subsequent to initial chromosomal breakages, new fusions follow and the breakage-bridge-fusion cycles continue. During this time, nonreciprocal translocations are generated. c-Myc-dependent remodeling of the organization of telomeres thus precedes the onset of genomic instability and subsequently leads to chromosomal rearrangements. Our findings reveal that c-Myc possesses the ability to structurally modify chromosomes through telomeric fusions, thereby reorganizing the genetic information.

Accurately characterizing the anomalous diffusion of a tracer particle has become a central issue in biophysics. However, measurement errors raise difficulty in the characterization of single trajectories, which is usually performed through the time-averaged mean square displacement (TAMSD). In this paper, we study a fractionally integrated moving average (FIMA) process as an appropriate model for anomalous diffusion data with measurement errors. We compare FIMA and traditional TAMSD estimators for the anomalous diffusion exponent. The ability of the FIMA framework to characterize dynamics in a wide range of anomalous exponents and noise levels through the simulation of a toy model (fractional Brownian motion disturbed by Gaussian white noise) is discussed. Comparison to the TAMSD technique, shows that FIMA estimation is superior in many scenarios. This is expected to enable new measurement regimes for single particle tracking (SPT) experiments even in the presence of high measurement errors.

Imaging of transcription by quantitative fluorescence-based techniques allows the examination of gene expression kinetics in single cells. Using a cell system for the in vivo visualization of mammalian mRNA transcriptional kinetics at single-gene resolution during the cell cycle, we previously demonstrated a reduction in transcription levels after replication. This phenomenon has been described as a homeostasis mechanism that buffers mRNA transcription levels with respect to the cell cycle stage and the number of transcribing alleles. Here, we examined how transcriptional buffering enforced during S phase affects two different promoters, the cytomegalovirus promoter versus the cyclin D1 promoter, that drive the same gene body. We found that global modulation of histone modifications could completely revert the transcription down-regulation imposed during replication. Furthermore, measuring these levels of transcriptional activity in fixed and living cells showed that the transcriptional potential of the genes was significantly higher than actual transcription levels, suggesting that promoters might normally be limited from reaching their full transcriptional potential.