A holistic understanding of tissue and organ structure and function requires the detection of molecular constituents in their original three-dimensional (3D) context. Imaging mass cytometry (IMC) enables simultaneous detection of up to 40 antigens and transcripts using metal-tagged antibodies but has so far been restricted to two-dimensional imaging. Here we report the development of 3D IMC for multiplexed 3D tissue analysis at single-cell resolution and demonstrate the utility of the technology by analysis of human breast cancer samples. The resulting 3D models reveal cellular and microenvironmental heterogeneity and cell-level tissue organization not detectable in two dimensions. 3D IMC will prove powerful in the study of phenomena occurring in 3D space such as tumor cell invasion and is expected to provide invaluable insights into cellular microenvironments and tissue architecture.
Multiplex tissue imaging enables in situ detection of more than 50 proteins or 1000 RNAs in the same tissue section with single cell resolution.
Analyses of spatially resolved cellular phenotypes, interactions, and neighborhoods have revealed mechanistic insights into physiological and pathological processes.
Multicellular, spatially informed signatures can serve as disease biomarkers and provide new targets for therapeutic development.
The complex determinants of health and disease can be determined when approached as a system of interactions of biological agents at different scales. Similar to the physicochemical properties that govern nucleic acids and proteins, there should be a finite set of rules that dictate the behavior of cells to form tissues. Thus, the occurrence of disease can be seen as flaws in processes that are governed by rules pertaining to multicellular structures. Multiplexed imaging is a technology that connects information that bridges multiple biological scales (i.e., molecules, cells, and tissues) and enables elucidation of rules associated with the formation of multicellular structures. Uncovering important multicellular structures associated with disease will propel a wave of development of new categories of diagnostics and therapeutics.
In mammography, calcifications are one of the most common signs of breast cancer. Detection of such lesions is an active area of research for computer-aided diagnosis and machine learning algorithms. Due to limited numbers of positive cases, many supervised detection models suffer from overfitting and fail to generalize. We present a one-class, semi-supervised framework using a deep convolutional autoencoder trained with over 50,000 images from 11,000 negative-only cases. Since the model learned from only normal breast parenchymal features, calcifications produced large signals when comparing the residuals between input and reconstruction output images. As a key advancement, a structural dissimilarity index was used to suppress non-structural noises. Our selected model achieved pixel-based AUROC of 0.959 and AUPRC of 0.676 during validation, where calcification masks were defined in a semi-automated process. Although not trained directly on any cancers, detection performance of calcification lesions on 1,883 testing images (645 malignant and 1238 negative) achieved 75% sensitivity at 2.5 false positives per image. Performance plateaued early when trained with only a fraction of the cases, and greater model complexity or a larger dataset did not improve performance. This study demonstrates the potential of this anomaly detection approach to detect mammographic calcifications in a semi-supervised manner with efficient use of a small number of labeled images, and may facilitate new clinical applications such as computer-aided triage and quality improvement.
The colorectal cancer (CRC)-associated microbiota creates a pro-tumorigenic intestinal milieu and shapes immune responses within the tumor microenvironment. However, how oncomicrobes – like Fusobacterium nucleatum, found in the oral cavity and associated with CRC tissues– affect these distinct aspects of tumorigenesis is difficult to parse. Herein, we found that neonatal inoculation of ApcMin/+ mice with F. nucleatum strain Fn7-1 circumvents technical barriers preventing its intestinal colonization, drives colonic Il17a expression prior to tumor formation, and potentiates intestinal tumorigenesis. Using gnotobiotic mice colonized with a minimal complexity microbiota (the altered Schaedler’s flora), we observed that intestinal Fn7-1 colonization increases colonic Th17 cell frequency and their IL-17A and IL-17F expression, along with a concurrent increase in colonic lamina propria Il23p19 expression. As Fn7-1 stably colonizes the intestinal tract in our models, we posited that microbial metabolites, specifically short-chain fatty acids (SCFA) that F. nucleatum abundantly produces in culture and, as we demonstrate, in the intestinal tract, might mediate part of its immunomodulatory effects in vivo. Supporting this hypothesis, we found that Fn7-1 did not alter RORγt+ CD4+T cell frequency in the absence of the SCFA receptor FFAR2. Taken together, our work suggests that F. nucleatum influences intestinal immunity by shaping Th17 responses in an FFAR2-dependent manner, although further studies are necessary to clarify the precise and multifaceted roles of FFAR2. The potential to increase intestinal Th17 responses is shared by another oncomicrobe, enterotoxigenic Bacteroides fragilis, highlighting a conserved pathway that could potentially be targeted to slow oncomicrobe-mediated CRC.
Image resolution in mass spectrometry imaging (MSI) is governed by the sampling probe, the motion of the stage relative to the probe, and the noise inherent for the sample and instrumentation employed. A new image formation model accounting for these variables is presented here. The model shows that the size of the probe, stage velocity, and the rate at which the probe consumes material from the surface govern the amount of blur present in the image. However, the main limiting factor for resolution is the signal-to-noise ratio (SNR). To evaluate blurring and noise effects, a new computational method for measuring lateral resolution in MSI is proposed. A spectral decomposition of the observed image signal and noise is used to determine a resolution number. To evaluate this technique, a silver step edge was prepared. This device was imaged at different pixels sizes using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI). A modulation transfer function (MTF) and a noise power spectrum (NPS) were computed for each single-ion image, and resolution was defined as the point of intersection between the MTF and the NPS. Finally, the algorithm was also applied to a MALDI MSI tissue data set.