ABSTRACT
Autofluorescence microscopy uses intrinsic sources of molecular contrast to provide cellular-level information without extrinsic labels. However, traditional cell segmentation tools are often optimized for high signal-to-noise ratio (SNR) images, such as fluorescently labeled cells, and unsurprisingly perform poorly on low SNR autofluorescence images. Therefore, new cell segmentation tools are needed for autofluorescence microscopy. Cellpose is a deep learning network that is generalizable across diverse cell microscopy images and automatically segments single cells to improve throughput and reduce inter-human biases. This study aims to validate Cellpose for autofluorescence imaging, specifically from multiphoton intensity images of NAD(P)H. Manually segmented nuclear masks of NAD(P)H images were used to train new Cellpose models. These models were applied to PANC-1 cells treated with metabolic inhibitors and patient-derived cancer organoids (across 9 patients) treated with chemotherapies. These datasets include co-registered fluorescence lifetime imaging microscopy (FLIM) of NAD(P)H and FAD, so fluorescence decay parameters and the optical redox ratio (ORR) were compared between masks generated by the new Cellpose model and manual segmentation. The Dice score between repeated manually segmented masks was significantly lower than that of repeated Cellpose masks (p<0.0001) indicating greater reproducibility between Cellpose masks. There was also a high correlation (R2>0.9) between Cellpose and manually segmented masks for the ORR, mean NAD(P)H lifetime, and mean FAD lifetime across 2D and 3D cell culture treatment conditions. Masks generated from Cellpose and manual segmentation also maintain similar means, variances, and effect sizes between treatments for the ORR and FLIM parameters. Overall, Cellpose provides a fast, reliable, reproducible, and accurate method to segment single cells in autofluorescence microscopy images such that functional changes in cells are accurately captured in both 2D and 3D culture.
ABSTRACT
Patients newly diagnosed with metastatic pancreatic ductal adenocarcinoma generally have poor survival, with heterogeneous rates of progression. Biomarkers that could predict progression and/or survival would help inform patients and providers as they make care decisions. In a previous retrospective study, we discovered that circulating thrombospondin-2 (THBS2) could, in combination with CA19-9, better distinguish patients with PDAC versus healthy controls. Here we evaluated whether THBS2 levels, previously not known to be prognostic, were associated with outcome in 68 patients at time of diagnosis of metastatic PDAC. Specifically, we interrogated the association of THBS2 level, alone or in combination with CA19-9, with progression by 90 days and/or survival to 180 days. The results indicate that elevated THBS2 levels alone, at the time of a metastatic PDAC diagnosis, can identify patients with a shorter time to death and thus help patients and providers when planning treatment.
ABSTRACT
Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed too late for effective therapy. The classic strategy for early detection biomarker advancement consists of initial retrospective phases of discovery and validation with tissue samples taken from individuals diagnosed with disease, compared with controls. Using this approach, we previously reported the discovery of a blood biomarker panel consisting of thrombospondin-2 (THBS2) and CA19-9 that together could discriminate resectable stage I and IIa PDAC as well as stages III and IV PDAC, with c-statistic values in the range of 0.96 to 0.97 in two phase II studies. We now report that in two studies of blood samples prospectively collected from 1 to 15 years prior to a PDAC diagnosis (Mayo Clinic and PLCO cohorts), THBS2 and/or CA19-9 failed to discriminate cases from healthy controls at the AUC = 0.8 needed. We conclude that PDAC progression may be heterogeneous and for some individuals can be more rapid than generally appreciated. It is important that PDAC early-detection studies incorporate high-risk, prospective prediagnostic cohorts into discovery and validation studies.Prevention Relevance: A blood biomarker panel of THBS2 and CA19-9 detects early stages of pancreatic ductal adenocarcinoma at diagnosis, but not when tested across a population up to 1 year earlier. Our findings suggest serial sampling over time, using prospectively collected samples for biomarker discovery, and more frequent screening of high-risk individuals.
