No abstract

Cancer-associated fibroblasts (CAFs) are abundantly present in the microenvironment of virtually all tumors and strongly impact tumor progression. Despite increasing insight into their function and heterogeneity, little is known regarding the origin of CAFs. Understanding the origin of CAF heterogeneity is needed to develop successful CAF-based targeted therapies. Through various transplantation studies in mice, we show that CAFs in both invasive lobular breast cancer and triple-negative breast cancer originate from mammary tissue-resident normal fibroblasts (NFs). Single-cell transcriptomics, in vivo and in vitro studies reveal the transition of CD26+ and CD26- NF populations into inflammatory CAFs (iCAFs) and myofibroblastic CAFs (myCAFs), respectively. Functional co-culture experiments show that CD26+ NFs transition into pro-tumorigenic iCAFs which recruit myeloid cells in a CXCL12-dependent manner and enhance tumor cell invasion via matrix-metalloproteinase (MMP) activity. Together, our data suggest that CD26+ and CD26- NFs transform into distinct CAF subpopulations in mouse models of breast cancer.

Cancer-associated fibroblasts (CAFs) are abundantly present in the microenvironment of virtually all tumors and strongly impact tumor progression. Despite increasing insight into their function and heterogeneity, little is known regarding the origin of CAFs. Understanding the origin of CAF heterogeneity is needed to develop successful CAF-based targeted therapies. Through various transplantation studies in mice we determined that CAFs in both invasive lobular breast cancer and triple negative breast cancer originate from mammary tissue-resident normal fibroblasts (NFs). Single-cell transcriptomics, in vivo tracing and in vitro studies revealed the transition of CD26+ and CD26- NF populations into inflammatory CAFs (iCAFs) and myofibroblastic CAFs (myCAFs), respectively. In vitro functional assays showed that CD26+ NFs transition into pro-tumorigenic iCAFs which recruit myeloid cells in a CXCL12-dependent manner and enhance tumor cell invasion via matrix-metalloproteinase (MMP) activity. Together, our data show that CD26+ and CD26- NFs transform into distinct CAF subpopulations in breast cancer.

In conventional flowcytometry one detector (primary) is dedicated for one fluorochrome. However, photons usually end up in other detectors too (fluorescence spillover). ‘Compensation’ is a process that corrects the spillover signal from all detectors except the primary detector. Post ‘compensation’, the photon counting error of spillover signals become evident as spreading of the data. The spreading induced by spillover impairs the ability to resolve stained cell population from the unstained one, potentially reducing or completely losing cell populations. For successful multi-color panel design, it is important to know the expected spillover to maximize the data resolution. The Spillover Spreading Matrix (SSM) can be used to estimate the spread, but the outcome is dependent on detector sensitivity. Simply, the same single stained sample produces different spillover spread values when detector(s) sensitivity is altered. Many researchers mistakenly use this artifact to “reduce” the spread by decreasing detector sensitivity. This can result in diminished capacity to resolve dimly expressing cell populations. Here, we introduce SQI (Spread Quantification Index), that can quantify the spillover spread independent of detector sensitivity and independent of dynamic range. This allows users to compare spillover spread between instruments having different types of detectors, which is not possible using SSM.

In flow cytometers, ideally each detector receives photons from one specific fluorochrome. However, photons usually end up in different detectors too (fluorescence spillover). Compensation is a process that removes this extra signal from all detectors other than the primary detector dedicated to that fluorochrome. Post compensation, the measurement error of spillover signals become evident as spreading of the data. Spillover reduces the ability to resolve single positive from double positive cell populations. For successful multi-color panel design, it is important to know the expected spillover. The Spillover Spread Matrix (SSM) can be used to estimate the spillover spread, but the outcome is heavily influenced by detector sensitivity. In short, the same single stained control sample produces different spillover spread values when detector sensitivity are altered. Many researchers unknowingly use this artifact to reduce the spread by decreasing detector sensitivity. This can result in reduced sensitivity and diminished capacity to resolve cell populations. In this article, we introduce Range as an alternative tool that can predict the spillover independent of detector sensitivity. Range is also independent of dynamic range, that allows to compare spread values between different types of instruments, something not possible using SSM.