Modeling glioblastoma heterogeneity as a dynamic network of cell states

Tumor cell heterogeneity is a crucial characteristic of malignant brain tumors and underpins phenomena such as therapy resistance and tumor recurrence. Advances in single-cell analysis have enabled the delineation of distinct cellular states of brain tumor cells, but the time-dependent changes in such states remain poorly understood. Here, we construct quantitative models of the time-dependent transcriptional variation of patient-derived glioblastoma (GBM) cells. We build the models by sampling and profiling barcoded GBM cells and their progeny over the course of 3\ua0weeks and by fitting a mathematical model to estimate changes in GBM cell states and their growth rates. Our model suggests a hierarchical yet plastic organization of GBM, where the rates and patterns of cell state switching are partly patient-specific. Therapeutic interventions produce complex dynamic effects, including inhibition of specific states and altered differentiation. Our method provides a general strategy to uncover time-dependent changes in cancer cells and offers a way to evaluate and predict how therapy affects cell state composition

COMBImage : a modular parallel processing framework for pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies

Background: Large-scale pairwise drug combination analysis has lately gained momentum in drug discovery and development projects, mainly due to the employment of advanced experimental-computational pipelines. This is fortunate as drug combinations are often required for successful treatment of complex diseases. Furthermore, most new drugs cannot totally replace the current standard-of-care medication, but rather have to enter clinical use as add-on treatment. However, there is a clear deficiency of computational tools for label-free and temporal image-based drug combination analysis that go beyond the conventional but relatively uninformative end point measurements. Results: COMBImage is a fast, modular and instrument independent computational framework for in vitro pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies. Jointly with automated analyses of temporal changes in cell morphology and confluence, it performs and displays conventional cell viability and synergy end point analyses. The image processing algorithms are parallelized using Google's MapReduce programming model and optimized with respect to method-specific tuning parameters. COMBImage is shown to process time-lapse microscopy movies from 384-well plates within minutes on a single quad core personal computer.This framework was employed in the context of an ongoing drug discovery and development project focused on glioblastoma multiforme; the most deadly form of brain cancer. Interesting add-on effects of two investigational cytotoxic compounds when combined with vorinostat were revealed on recently established clonal cultures of glioma-initiating cells from patient tumor samples. Therapeutic synergies, when normal astrocytes were used as a toxicity cell model, reinforced the pharmacological interest regarding their potential clinical use. Conclusions: COMBImage enables, for the first time, fast and optimized pairwise drug combination analyses of temporal changes in label-free video microscopy movies. Providing this jointly with conventional cell viability based end point analyses, it could help accelerating and guiding any drug discovery and development project, without use of cell labeling and the need to employ a particular live cell imaging instrument

COMBImage2 : a parallel computational framework for higher-order drug combination analysis that includes automated plate design, matched filter based object counting and temporal data mining

Background: Pharmacological treatment of complex diseases using more than two drugs is commonplace in the clinic due to better efficacy, decreased toxicity and reduced risk for developing resistance. However, many of these higher-order treatments have not undergone any detailed preceding in vitro evaluation that could support their therapeutic potential and reveal disease related insights. Despite the increased medical need for discovery and development of higher-order drug combinations, very few reports from systematic large-scale studies along this direction exist. A major reason is lack of computational tools that enable automated design and analysis of exhaustive drug combination experiments, where all possible subsets among a panel of pre-selected drugs have to be evaluated. Results: Motivated by this, we developed COMBImage2, a parallel computational framework for higher-order drug combination analysis. COMBImage2 goes far beyond its predecessor COMBImage in many different ways. In particular, it offers automated 384-well plate design, as well as quality control that involves resampling statistics and inter-plate analyses. Moreover, it is equipped with a generic matched filter based object counting method that is currently designed for apoptotic-like cells. Furthermore, apart from higher-order synergy analyses, COMBImage2 introduces a novel data mining approach for identifying interesting temporal response patterns and disentangling higher- from lower- and single-drug effects.COMBImage2 was employed in the context of a small pilot study focused on the CUSP9v4 protocol, which is currently used in the clinic for treatment of recurrent glioblastoma. For the first time, all 246 possible combinations of order 4 or lower of the 9 single drugs consisting the CUSP9v4 cocktail, were evaluated on an in vitro clonal culture of glioma initiating cells. Conclusions: COMBImage2 is able to automatically design and robustly analyze exhaustive and in general higher-order drug combination experiments. Such a versatile video microscopy oriented framework is likely to enable, guide and accelerate systematic large-scale drug combination studies not only for cancer but also other diseases

Platelet-derived growth factor over-expression in retinal progenitors results in abnormal retinal vessel formation

Platelet-derived growth factor (PDGF) plays an important role in development of the central nervous system, including the retina. Excessive PDGF signaling is associated with proliferative retinal disorders. We reported previously that transgenic mice in which PDGF-B was over-expressed under control of the nestin enhancer, nes/tk-PdgfB-lacZ, exhibited enhanced apoptosis in the developing corpus striatum. These animals display enlarged lateral ventricles after birth as well as behavioral aberrations as adults. Here, we report that in contrast to the relatively mild central nervous system phenotype, development of the retina is severely disturbed in nes/tk-PdgfB-lacZ mice. In transgenic retinas all nuclear layers were disorganized and photoreceptor segments failed to develop properly. Since astrocyte precursor cells did not populate the retina, retinal vascular progenitors could not form a network of vessels. With time, randomly distributed vessels resembling capillaries formed, but there were no large trunk vessels and the intraocular pressure was reduced. In addition, we observed a delayed regression of the hyaloid vasculature. The prolonged presence of this structure may contribute to the other abnormalities observed in the retina, including the defective lamination

Platelet-Derived Growth Factor Over-Expression in Retinal Progenitors Results in Abnormal Retinal Vessel Formation

<div><p>Platelet-derived growth factor (PDGF) plays an important role in development of the central nervous system, including the retina. Excessive PDGF signaling is associated with proliferative retinal disorders. We reported previously that transgenic mice in which PDGF-B was over-expressed under control of the nestin enhancer, nes/tk-PdgfB-lacZ, exhibited enhanced apoptosis in the developing corpus striatum. These animals display enlarged lateral ventricles after birth as well as behavioral aberrations as adults. Here, we report that in contrast to the relatively mild central nervous system phenotype, development of the retina is severely disturbed in nes/tk-PdgfB-lacZ mice. </p> <p>In transgenic retinas all nuclear layers were disorganized and photoreceptor segments failed to develop properly. Since astrocyte precursor cells did not populate the retina, retinal vascular progenitors could not form a network of vessels. With time, randomly distributed vessels resembling capillaries formed, but there were no large trunk vessels and the intraocular pressure was reduced. In addition, we observed a delayed regression of the hyaloid vasculature. The prolonged presence of this structure may contribute to the other abnormalities observed in the retina, including the defective lamination.</p> </div

Morphological alterations in eyes of mice over-expressing PDGF-B.

<p>Eyes from wild type (A) and transgenic (B) mice on postnatal day 15 (P15) were photographed after dissection. Folding was observed in the transgenic (D) retina, compare to wild type (C). The eye-pairs showed in (A) and (B) are from the same individual, respectively, and the retinas shown in (C) and (D) were dissected from the eyes shown in (A) and (B, large eye). Histological examination (DAPI nuclear stain) revealed a disorganization of retinal lamination (F) not observed in wild type eyes (E). Outer nuclear layer (onl), inner nuclear layer (inl), ganglion cell layer (gcl), Scale bar 50 µm.</p

Partial restoration of Pax2+ and CD31+ migration after STI571 treatment.

<p>Retina flat-mounts of postnatal day 1 (P1) or P2 mice whose mother received the tyrosine kinase inhibitor STI571 (Glivec) from E17.5 to birth (B, B′), or from E17.5 to birth and at P1 (C, C′) and P2 control mice (A, A′). In panels D–G, the inhibitor was administrated to wild-type (D, D′, E, E′) or transgenic (F, F′, G, G′) mice between P7 to P14. Cross sections of retina were stained with antibodies to CD31 and NG2. Outer nuclear layer (onl), inner nuclear layer (inl), ganglion cell layer (gcl). Scale bar A–C, A′–C′ 200 µm, D–G, D′–G′ 100 µm.</p

Failure to vascularize the retina.

<p>Staining of cross-sections and flat-mounts with antibodies against CD31, NG2 and GFAP on postnatal day 5 (P5, (A–F, M–O) and P60 (G–L). The white arrowhead in A and C indicates the border of CD31-positive cells in a control mouse. Note their absence in the transgenic retina (B and D). NG2, CD31 and GFAP staining is localized to the retrolental cell mass, indicative of the hyaloid, in transgenic retinas (D, F, M–O) compared to wild-type retinas (C, E). Irregular CD31+ cells had developed at P60 in the transgenic retina (H, J, L). Wild-type control (G, I, K). M–O: The same P5 transgenic retina as shown in D, stained for NG2 (red) and CD31 (green) and DAPI (blue). Tractional forces on the retina have caused it to fold (thin white arrow in M) by sprouting ectopic and irregular blood vessels (black arrows in N) that infiltrates the retina at various depths. Outer nuclear layer (onl), inner nuclear layer (inl), ganglion cell layer (gcl), optic nerve exit (one). Scale bar A and B 1 µm, C–F 200 µm, G–H 1 mm, I–J 200 µm, K–L 50 µm and M 200 µm.</p

Glial activation in the PDGF-B transgenic retina.

<p>Retinas from wild-type (A, C) and transgenic (B, D) mice were stained for Pax2 on postnatal day 1 (P1) and P5 to depict astrocyte precursors. Mature astrocytes were visualized with GFAP antibodies on retinal flat-mounts from wild-type (E) and transgenic mice (F). Panels G and H are magnifications of E and F. The insert in H shows astrocyte morphology in the transgenic retina (the same cells are indicated by an arrow). Scale bar A–D 200 µm, insets 100 µm, E–F 1 mm, G–H 100 µm.</p