Reproducible brain-wide association studies require thousands of individuals.

Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions1-3 (for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping4 (the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain-behavioural phenotype associations5,6. Here we used three of the largest neuroimaging datasets currently available-with a total sample size of around 50,000 individuals-to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain-phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.

Salmonella Persist in Activated Macrophages in T Cell-Sparse Granulomas but Are Contained by Surrounding CXCR3 Ligand-Positioned Th1 Cells.

Salmonella enterica (Se) bacteria cause persistent intracellular infections while stimulating a robust interferon-γ-producing CD4+ T (Th1) cell response. We addressed this paradox of concomitant infection and immunity by tracking fluorescent Se organisms in mice. Se bacteria persisted in nitric oxide synthase (iNOS)-producing resident and recruited macrophages while inducing genes related to protection from nitric oxide. Se-infected cells occupied iNOS+ splenic granulomas that excluded T cells but were surrounded by mononuclear phagocytes producing the chemokines CXCL9 and CXCL10, and Se epitope-specific Th1 cells expressing CXCR3, the receptor for these chemokines. Blockade of CXCR3 inhibited Th1 occupancy of CXCL9/10-dense regions, reduced activation of the Th1 cells, and led to increased Se growth. Thus, intracellular Se bacteria survive in their hosts by counteracting toxic products of the innate immune response and by residing in T cell-sparse granulomas, away from abundant Th1 cells positioned via CXCR3 in a bordering region that act to limit infection.

A biologist's guide to planning and performing quantitative bioimaging experiments.

Technological advancements in biology and microscopy have empowered a transition from bioimaging as an observational method to a quantitative one. However, as biologists are adopting quantitative bioimaging and these experiments become more complex, researchers need additional expertise to carry out this work in a rigorous and reproducible manner. This Essay provides a navigational guide for experimental biologists to aid understanding of quantitative bioimaging from sample preparation through to image acquisition, image analysis, and data interpretation. We discuss the interconnectedness of these steps, and for each, we provide general recommendations, key questions to consider, and links to high-quality open-access resources for further learning. This synthesis of information will empower biologists to plan and execute rigorous quantitative bioimaging experiments efficiently.

Best practices and tools for reporting reproducible fluorescence microscopy methods.

Although fluorescence microscopy is ubiquitous in biomedical research, microscopy methods reporting is inconsistent and perhaps undervalued. We emphasize the importance of appropriate microscopy methods reporting and seek to educate researchers about how microscopy metadata impact data interpretation. We provide comprehensive guidelines and resources to enable accurate reporting for the most common fluorescence light microscopy modalities. We aim to improve microscopy reporting, thus improving the quality, rigor and reproducibility of image-based science.

Tissue-Like 3D Standard and Protocols for Microscope Quality Management.

This article outlines a global study conducted by the Association of Biomedical Resource Facilities (ABRF) Light Microscopy Research Group (LMRG). The results present a novel 3D tissue-like biologically relevant standard sample that is affordable and straightforward to prepare. Detailed sample preparation, instrument-specific image acquisition protocols and image analysis methods are presented and made available to the community. The standard consists of sub-resolution and large well characterized relative intensity fluorescence microspheres embedded in a 120 µm thick 3D gel with a refractive index of 1.365. The standard allows the evaluation of several properties as a function of depth. These include the following: 1) microscope resolution with automated analysis of the point-spread function (PSF), 2) automated signal-to-noise ratio analysis, 3) calibration and correction of fluorescence intensity loss, and 4) quantitative relative intensity. Results demonstrate expected refractive index mismatch dependent losses in intensity and resolution with depth, but the relative intensities of different objects at similar depths are maintained. This is a robust standard showing reproducible results across laboratories, microscope manufacturers and objective lens types (e.g., magnification, immersion medium). Thus, these tools will be valuable for the global community to benchmark fluorescence microscopes and will contribute to improved scientific rigor and reproducibility.

Human induced pluripotent stem cells integrate, create synapses and extend long axons after spinal cord injury.

Numerous interventions have been explored in animal models using cells differentiated from human induced pluripotent stem cells (iPSCs) in the context of neural injury with some success. Our work seeks to transplant cells that are generated from hiPSCs into regionally specific spinal neural progenitor cells (sNPCs) utilizing a novel accelerated differentiation protocol designed for clinical translation. We chose a xenotransplantation model because our laboratory is focused on the behaviour of human cells in order to bring this potential therapy to translation. Cells were transplanted into adult immunodeficient rats after moderate contusion spinal cord injury (SCI). Twelve weeks later, cells derived from the transplanted sNPCs survived and differentiated into neurons and glia that filled the lesion cavity and produced a thoracic spinal cord transcriptional program in vivo. Furthermore, neurogenesis and ionic channel expression were promoted within the adjacent host spinal cord tissue. Transplanted cells displayed robust integration properties including synapse formation and myelination by host oligodendrocytes. Axons from transplanted hiPSC sNPC-derived cells extended both rostrally and caudally from the SCI transplant site, rostrally approximately 6 cm into supraspinal structures. Thus, iPSC-derived sNPCs may provide a patient-specific cell source for patients with SCI that could provide a relay system across the site of injury.

Super-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy software.

With the widespread uptake of two-dimensional (2D) and three-dimensional (3D) single-molecule localization microscopy (SMLM), a large set of different data analysis packages have been developed to generate super-resolution images. In a large community effort, we designed a competition to extensively characterize and rank the performance of 2D and 3D SMLM software packages. We generated realistic simulated datasets for popular imaging modalities-2D, astigmatic 3D, biplane 3D and double-helix 3D-and evaluated 36 participant packages against these data. This provides the first broad assessment of 3D SMLM software and provides a holistic view of how the latest 2D and 3D SMLM packages perform in realistic conditions. This resource allows researchers to identify optimal analytical software for their experiments, allows 3D SMLM software developers to benchmark new software against the current state of the art, and provides insight into the current limits of the field.

Publisher Correction: Super-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy software.

In the version of this paper originally published, Figure 4a contained errors that were introduced during typesetting. The bottom 11° ThunderSTORM image is an xz view but was incorrectly labeled as xy, and the low x-axis value in the four line profiles was incorrectly set as -60 instead of -50. These errors have been corrected in the PDF and HTML versions of the paper.

Associations between tissue-based CD3+ T-lymphocyte count and colorectal cancer survival in a prospective cohort of older women.

Tumor-infiltrating lymphocytes in colorectal cancer (CRC) predict better survival. However, associations between T-lymphocyte count in histologically normal tissues from patients with CRC and survival remain uncertain. We examined associations of CD3+ T-cells in colorectal tumor and histologically normal tissues with CRC-specific and all-cause mortality in the prospective Iowa Women's Health Study. Tissue microarrays were constructed using paraffin-embedded colorectal tissue samples from 464 women with tumor tissues and 314 women with histologically normal tissues (55-69 years at baseline) diagnosed with incident CRC from 1986 to 2002 and followed through 2014 (median follow-up 20.5 years). Three tumor and two histologically normal tissue cores for each patient were immunostained using CD3+ antibody and quantified, and the counts were averaged across the cores in each tissue. Cox proportional hazards regression estimated hazard ratios (HR) and 95% confidence interval (CI) for CRC-specific and all-cause mortality. After adjustment for age at diagnosis, body mass index, smoking status, tumor grade, and stage, HRs (95% CI) for the highest versus lowest tertile of tumor CD3+ score were 0.59 (0.38-0.89) for CRC-specific mortality and 0.82 (0.63-1.05) for all-cause mortality; for histologically normal CD3+ score, the corresponding HRs (95% CI) were 0.47 (0.19-1.17) and 0.50 (0.27-0.90), respectively. The CD3+ score combining the tumor and histologically normal scores was inversely associated with CRC-specific and all-cause mortality. Although the association between tumor CD3+ score and all-cause mortality was not significant, both higher CD3+ T-lymphocyte counts in tumor and histologically normal scores tended to be associated with lower CRC-specific and all-cause mortality.

Chrysalis: A New Method for High-Throughput Histo-Cytometry Analysis of Images and Movies.

Advances in imaging have led to the development of powerful multispectral, quantitative imaging techniques, like histo-cytometry. The utility of this approach is limited, however, by the need for time consuming manual image analysis. We therefore developed the software Chrysalis and a group of Imaris Xtensions to automate this process. The resulting automation allowed for high-throughput histo-cytometry analysis of three-dimensional confocal microscopy and two-photon time-lapse images of T cell-dendritic cell interactions in mouse spleens. It was also applied to epi-fluorescence images to quantify T cell localization within splenic tissue by using a "signal absorption" strategy that avoids computationally intensive distance measurements. In summary, this image processing and analysis software makes histo-cytometry more useful for immunology applications by automating image analysis.

TCR Affinity Biases Th Cell Differentiation by Regulating CD25, Eef1e1, and Gbp2.

Naive CD4+ T lymphocytes differentiate into various Th cell subsets following TCR binding to microbial peptide:MHC class II (p:MHCII) complexes on dendritic cells (DCs). The affinity of the TCR interaction with p:MHCII plays a role in Th differentiation by mechanisms that are not completely understood. We found that low-affinity TCRs biased mouse naive T cells to become T follicular helper (Tfh) cells, whereas higher-affinity TCRs promoted the formation of Th1 or Th17 cells. We explored the basis for this phenomenon by focusing on IL-2R signaling, which is known to promote Th1 and suppress Tfh cell differentiation. SIRP⍺+ DCs produce abundant p:MHCII complexes and consume IL-2, whereas XCR1+ DCs weakly produce p:MHCII but do not consume IL-2. We found no evidence, however, of preferential interactions between Th1 cell-prone, high-affinity T cells and XCR1+ DCs or Tfh cell-prone, low-affinity T cells and SIRP⍺+ DCs postinfection with bacteria expressing the peptide of interest. Rather, high-affinity T cells sustained IL-2R expression longer and expressed two novel Th cell differentiation regulators, Eef1e1 and Gbp2, to a higher level than low-affinity T cells. These results suggest that TCR affinity does not influence Th cell differentiation by biasing T cell interactions with IL-2-consuming DCs, but instead, directly regulates genes in naive T cells that control the differentiation process.

Quantitative evaluation of software packages for single-molecule localization microscopy.

The quality of super-resolution images obtained by single-molecule localization microscopy (SMLM) depends largely on the software used to detect and accurately localize point sources. In this work, we focus on the computational aspects of super-resolution microscopy and present a comprehensive evaluation of localization software packages. Our philosophy is to evaluate each package as a whole, thus maintaining the integrity of the software. We prepared synthetic data that represent three-dimensional structures modeled after biological components, taking excitation parameters, noise sources, point-spread functions and pixelation into account. We then asked developers to run their software on our data; most responded favorably, allowing us to present a broad picture of the methods available. We evaluated their results using quantitative and user-interpretable criteria: detection rate, accuracy, quality of image reconstruction, resolution, software usability and computational resources. These metrics reflect the various tradeoffs of SMLM software packages and help users to choose the software that fits their needs.

Aptamer micelles targeting fractalkine-expressing cancer cells in vitro and in vivo.

In this work we hypothesized that the chemokine fractalkine can serve as a cancer molecular target. We engineered aptamer micelles functionalized with an outer poly(ethylene glycol) (PEG) corona, and investigated the extent and efficacy of using them as a targeting tool against fractalkine-expressing colon adenocarcinoma cells. In vitro cell binding results showed that aptamer micelles bound and internalized to fractalkine-expressing cancer cells with the majority of the micelles found free in the cytoplasm. Minimal surface binding was observed by healthy cells. Even though partial PEGylation did not prevent serum adsorption, micelles were highly resistant to endonuclease and exonuclease degradation. In vivo biodistribution studies and confocal studies demonstrated that even though both aptamer and control micelles showed tumor accumulation, only the aptamer micelles internalized into fractalkine-expressing cancer cells, thus demonstrating the potential of the approach and showing that fractalkine may serve as a specific target for nanoparticle delivery to cancer cells.

High throughput 3D super-resolution microscopy reveals Caulobacter crescentus in vivo Z-ring organization.

We created a high-throughput modality of photoactivated localization microscopy (PALM) that enables automated 3D PALM imaging of hundreds of synchronized bacteria during all stages of the cell cycle. We used high-throughput PALM to investigate the nanoscale organization of the bacterial cell division protein FtsZ in live Caulobacter crescentus. We observed that FtsZ predominantly localizes as a patchy midcell band, and only rarely as a continuous ring, supporting a model of "Z-ring" organization whereby FtsZ protofilaments are randomly distributed within the band and interact only weakly. We found evidence for a previously unidentified period of rapid ring contraction in the final stages of the cell cycle. We also found that DNA damage resulted in production of high-density continuous Z-rings, which may obstruct cytokinesis. Our results provide a detailed quantitative picture of in vivo Z-ring organization.

PALMsiever: a tool to turn raw data into results for single-molecule localization microscopy.

During the past decade, localization microscopy (LM) has transformed into an accessible, commercially available technique for life sciences. However, data processing can be challenging to the non-specialist and care is still needed to produce meaningful results. PALMsiever has been developed to provide a user-friendly means of visualizing, filtering and analyzing LM data. It includes drift correction, clustering, intelligent line profiles, many rendering algorithms and 3D data visualization. It incorporates the main analysis and data processing modalities used by experts in the field, as well as several new features we developed, and makes them broadly accessible. It can easily be extended via plugins and is provided as free of charge open-source software.

Quantitative super-resolution imaging reveals protein stoichiometry and nanoscale morphology of assembling HIV-Gag virions.

The HIV structural protein Gag assembles to form spherical particles of radius ∼70 nm. During the assembly process, the number of Gag proteins increases over several orders of magnitude from a few at nucleation to thousands at completion. The challenge in studying protein assembly lies in the fact that current methods such as standard fluorescence or electron microscopy techniques cannot access all stages of the assembly process in a cellular context. Here, we demonstrate an approach using super-resolution fluorescence imaging that permits quantitative morphological and molecular counting analysis over a wide range of protein cluster sizes. We applied this technique to the analysis of hundreds of HIV-Gag clusters at the cellular plasma membrane, thus elucidating how different fluorescent labels can change the assembly of virions.

Spatial mapping of cellular senescence: emerging challenges and opportunities.

Cellular senescence is a well-established driver of aging and age-related diseases. There are many challenges to mapping senescent cells in tissues such as the absence of specific markers and their relatively low abundance and vast heterogeneity. Single-cell technologies have allowed unprecedented characterization of senescence; however, many methodologies fail to provide spatial insights. The spatial component is essential, as senescent cells communicate with neighboring cells, impacting their function and the composition of extracellular space. The Cellular Senescence Network (SenNet), a National Institutes of Health (NIH) Common Fund initiative, aims to map senescent cells across the lifespan of humans and mice. Here, we provide a comprehensive review of the existing and emerging methodologies for spatial imaging and their application toward mapping senescent cells. Moreover, we discuss the limitations and challenges inherent to each technology. We argue that the development of spatially resolved methods is essential toward the goal of attaining an atlas of senescent cells.

TNTdetect.AI: A Deep Learning Model for Automated Detection and Counting of Tunneling Nanotubes in Microscopy Images.

BACKGROUND: Tunneling nanotubes (TNTs) are cellular structures connecting cell membranes and mediating intercellular communication. TNTs are manually identified and counted by a trained investigator; however, this process is time-intensive. We therefore sought to develop an automated approach for quantitative analysis of TNTs. METHODS: We used a convolutional neural network (U-Net) deep learning model to segment phase contrast microscopy images of both cancer and non-cancer cells. Our method was composed of preprocessing and model development. We developed a new preprocessing method to label TNTs on a pixel-wise basis. Two sequential models were employed to detect TNTs. First, we identified the regions of images with TNTs by implementing a classification algorithm. Second, we fed parts of the image classified as TNT-containing into a modified U-Net model to estimate TNTs on a pixel-wise basis. RESULTS: The algorithm detected 49.9% of human expert-identified TNTs, counted TNTs, and calculated the number of TNTs per cell, or TNT-to-cell ratio (TCR); it detected TNTs that were not originally detected by the experts. The model had 0.41 precision, 0.26 recall, and 0.32 f-1 score on a test dataset. The predicted and true TCRs were not significantly different across the training and test datasets (p = 0.78). CONCLUSIONS: Our automated approach labeled and detected TNTs and cells imaged in culture, resulting in comparable TCRs to those determined by human experts. Future studies will aim to improve on the accuracy, precision, and recall of the algorithm.