a-label-free-segmentation-approach-for-intravital-imaging-mammary

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

The ability to visualize complex and dynamic physiological interactions between numerous cell types and the extracellular matrix (ECM) within a live tumor ...

University of Wisconsin-Madison

High mammographic density has been correlated with a 4-fold to 6-fold increased risk of developing breast cancer, and is associated with increased stromal deposition of extracellular matrix proteins, including collagen I. The molecular and cellular mechanisms responsible for high breast tissue density are not completely understood.

Neutrophils drive accelerated tumor progression in the collagen-dense mammary tumor microenvironment.

In response to chemical stimuli from cancer cells, mesenchymal stem cells (MSC) can differentiate into cancer-associated fibroblasts (CAF) and promote tumor progression. How mechanical stimuli such as stiffness of the extracellular matrix (ECM) contribute to MSC phenotype in cancer remains poorly understood. Here, we show that ECM stiffness leads to mechano-signal transduction in MSC, which promotes mammary tumor growth in part through secretion of the signaling protein prosaposin. On a stiff matrix, MSC cultured with conditioned media from mammary cancer cells expressed increased levels of α-smooth muscle actin, a marker of CAF, compared with MSC cultured on a soft matrix. By contrast, MSC cultured on a stiff matrix secreted prosaposin that promoted proliferation and survival of mammary carcinoma cells but inhibited metastasis. Our findings suggest that in addition to chemical stimuli, increased stiffness of the ECM in the tumor microenvironment induces differentiation of MSC to CAF, triggering enhanced proliferation and survival of mammary cancer cells. .

Mechano-Signal Transduction in Mesenchymal Stem Cells Induces Prosaposin Secretion to Drive the Proliferation of Breast Cancer Cells.

Quantification of fibrillar collagen organization has given new insight into the possible role of collagen topology in many diseases and has also identified candidate image-based bio-markers in breast cancer and pancreatic cancer. We have been developing collagen quantification tools based on the curvelet transform (CT) algorithm and have demonstrated this to be a powerful multiscale image representation method due to its unique features in collagen image denoising and fiber edge enhancement. In this paper, we present our CT-based collagen quantification software platform with a focus on new features and also giving a detailed description of curvelet-based fiber representation. These new features include C++-based code optimization for fast individual fiber tracking, Java-based synthetic fiber generator module for method validation, automatic tumor boundary generation for fiber relative quantification, parallel computing for large-scale batch mode processing, region-of-interest analysis for user-specified quantification, and pre- and post-processing modules for individual fiber visualization. We present a validation of the tracking of individual fibers and fiber orientations by using synthesized fibers generated by the synthetic fiber generator. In addition, we provide a comparison of the fiber orientation calculation on pancreatic tissue images between our tool and three other quantitative approaches. Lastly, we demonstrate the use of our software tool for the automatic tumor boundary creation and the relative alignment quantification of collagen fibers in human breast cancer pathology images, as well as the alignment quantification of  mouse xenograft breast cancer images.

Fibrillar Collagen Quantification With Curvelet Transform Based Computational Methods.

This study uses dynamic hyperpolarized [1-C]pyruvate magnetic resonance spectroscopic imaging (MRSI) to estimate differences in glycolytic metabolism between highly metastatic (4T1,  = 7) and metastatically dormant (4T07,  = 7) murine breast cancer models. The apparent conversion rate of pyruvate-to-lactate (k) and lactate-to-pyruvate area-under-the-curve ratio (AUC) were estimated from the metabolite images and compared with biochemical metabolic measures and immunohistochemistry (IHC). A non-significant trend of increasing k ( = 0.17) and AUC ( = 0.11) from 4T07 to 4T1 tumors was observed. No significant differences in tumor IHC lactate dehydrogenase-A (LDHA), monocarboxylate transporter-1 (MCT1), cluster of differentiation 31 (CD31), and hypoxia inducible factor-α (HIF-1α), tumor lactate-dehydrogenase (LDH) activity, or blood lactate or glucose levels were found between the two tumor lines. However, AUC was significantly correlated with tumor LDH activity ( = 0.621,  = 0.027) and blood glucose levels ( = -0.474,  = 0.042). k displayed a similar, non-significant trend for LDH activity ( = 0.480,  = 0.114) and blood glucose levels ( = -0.414,  = 0.088). Neither k nor AUC were significantly correlated with blood lactate levels or tumor LDHA or MCT1. The significant positive correlation between AUC and tumor LDH activity indicates the potential of AUC as a biomarker of glycolytic metabolism in breast cancer models. However, the lack of a significant difference between in vivo tumor metabolism for the two models suggest similar pyruvate-to-lactate conversion despite differing metastatic potential.

Hyperpolarized C Magnetic Resonance Spectroscopic Imaging of Pyruvate Metabolism in Murine Breast Cancer Models of Different Metastatic Potential.

It is well established that collagen alignment in the breast tumor microenvironment provides biophysical cues to drive disease progression. Numerous mechanistic studies have demonstrated that tumor cell behavior is driven by the architecture and stiffness of the collagen matrix. However, the mechanical properties within a 3D collagen microenvironment, particularly at the scale of the cell, remain poorly defined. To investigate cell-scale mechanical cues with respect to local collagen architecture, we employed a combination of intravital imaging of the mammary tumor microenvironment and a 3D collagen gel system with both acellular pNIPAAm microspheres and MDA-MB-231 breast carcinoma cells. Within the in vivo tumor microenvironment, the displacement of collagen fiber was identified in response to tumor cells migrating through the stromal matrix. To further investigate cell-scale stiffness in aligned fiber architectures and the propagation of cell-induced fiber deformations, precise control of collagen architecture was coupled with innovative methodology to measure mechanical properties of the collagen fiber network. This method revealed up to a 35-fold difference in directional cell-scale stiffness resulting from contraction against aligned fibers. Furthermore, the local anisotropy of the matrix dramatically altered the rate at which contractility-induced fiber displacements decayed over distance. Together, our results reveal mechanical properties in aligned matrices that provide dramatically different cues to the cell in perpendicular directions. These findings are supported by the mechanosensing behavior of tumor cells and have important implications for cell-cell communication within the tissue microenvironment. STATEMENT OF SIGNIFICANCE: It is widely appreciated that the architecture of the extracellular matrix impacts cellular behavior in normal and disease states. Numerous studies have determined the fundamental role of collagen matrix architecture on cellular mechanosensing, but effectively quantifying anisotropic mechanical properties of the collagen matrix at the cell-scale remains challenging. Here, we developed innovative methodology to discover that collagen alignment results in a 35-fold difference in cell-scale stiffness and alters contractile force transmission through the fiber network. Furthermore, we identified bias in cell response along the axis of alignment, where local stiffness is highest. Overall, our results define cell-scale stiffness and fiber deformations due to collagen architecture that may instruct cell communication within a broad range of tissue microenvironments.

Directional cues in the tumor microenvironment due to cell contraction against aligned collagen fibers.

Exosomes are small extracellular vesicles (EVs) secreted by all cells and found in biological fluids, which can serve as minimally invasive liquid biopsies with extremely high therapeutic and diagnostic potential. Mass spectrometry (MS)-based proteomics is a powerful technique to profile and quantify the protein content in exosomes, but the current methods require laborious and time-consuming multistep sample preparation that significantly limit throughput. Herein, we report a one-pot exosome proteomics method enabled by a photocleavable surfactant, Azo, to simplify exosomal lysis, effectively extract proteins, and expedite digestion. We have applied this method to exosomes derived from isolated mammary fibroblasts and confidently identified 3466 proteins and quantified 2288 proteins using a reversed-phase liquid chromatography coupled to trapped ion mobility spectrometry (TIMS) quadrupole time-of-flight mass spectrometer. Here, 3166 (91%) of the identified proteins are annotated in the exosome/EVs databases, ExoCarta and Vesiclepedia, including important exosomal markers, CD63, PDCD6IP, and SDCBP. This method is fast, simple, and highly effective at extracting exosomal proteins with high reproducibility for deep exosomal proteome coverage. We envision that this method could be generally applicable for exosome proteomics applications in biomedical research, therapeutic interventions, and clinical diagnostics.

David R. Inman

Department of Cell and Regenerative Biology

University of Wisconsin-Madison

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

David R. Inman

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Cell and Regenerative Biology

University of Wisconsin-Madison

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Department of Cell and Regenerative Biology