Modeling cell proliferation in human acute myeloid leukemia xenografts.

MOTIVATION: Acute myeloid leukemia (AML) is one of the most common hematological malignancies, characterized by high relapse and mortality rates. The inherent intra-tumor heterogeneity in AML is thought to play an important role in disease recurrence and resistance to chemotherapy. Although experimental protocols for cell proliferation studies are well established and widespread, they are not easily applicable to in vivo contexts, and the analysis of related time-series data is often complex to achieve. To overcome these limitations, model-driven approaches can be exploited to investigate different aspects of cell population dynamics. RESULTS: In this work, we present ProCell, a novel modeling and simulation framework to investigate cell proliferation dynamics that, differently from other approaches, takes into account the inherent stochasticity of cell division events. We apply ProCell to compare different models of cell proliferation in AML, notably leveraging experimental data derived from human xenografts in mice. ProCell is coupled with Fuzzy Self-Tuning Particle Swarm Optimization, a swarm-intelligence settings-free algorithm used to automatically infer the models parameterizations. Our results provide new insights on the intricate organization of AML cells with highly heterogeneous proliferative potential, highlighting the important role played by quiescent cells and proliferating cells characterized by different rates of division in the progression and evolution of the disease, thus hinting at the necessity to further characterize tumor cell subpopulations. AVAILABILITY AND IMPLEMENTATION: The source code of ProCell and the experimental data used in this work are available under the GPL 2.0 license on GITHUB at the following URL: https://github.com/aresio/ProCell. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Molecular mechanisms of asymmetric divisions in mammary stem cells.

Stem cells have the remarkable ability to undergo proliferative symmetric divisions and self-renewing asymmetric divisions. Balancing of the two modes of division sustains tissue morphogenesis and homeostasis. Asymmetric divisions of Drosophila neuroblasts (NBs) and sensory organ precursor (SOP) cells served as prototypes to learn what we consider now principles of asymmetric mitoses. They also provide initial evidence supporting the notion that aberrant symmetric divisions of stem cells could correlate with malignancy. However, transferring the molecular knowledge of circuits underlying asymmetry from flies to mammals has proven more challenging than expected. Several experimental approaches have been used to define asymmetry in mammalian systems, based on daughter cell fate, unequal partitioning of determinants and niche contacts, or proliferative potential. In this review, we aim to provide a critical evaluation of the assays used to establish the stem cell mode of division, with a particular focus on the mammary gland system. In this context, we will discuss the genetic alterations that impinge on the modality of stem cell division and their role in breast cancer development.

Barcode demultiplexing of nanopore sequencing raw signals by unsupervised machine learning.

Introduction: Oxford Nanopore Technologies (ONT) is a third generation sequencing approach that allows the analysis of individual, full-length nucleic acids. ONT records the alterations of an ionic current flowing across a nano-scaled pore while a DNA or RNA strand is threading through the pore. Basecalling methods are then leveraged to translate the recorded signal back to the nucleic acid sequence. However, basecall generally introduces errors that hinder the process of barcode demultiplexing, a pivotal task in single-cell RNA sequencing that allows for separating the sequenced transcripts on the basis of their cell of origin. Methods: To solve this issue, we present a novel framework, called UNPLEX, designed to tackle the barcode demultiplexing problem by operating directly on the recorded signals. UNPLEX combines two unsupervised machine learning methods: autoencoders and self-organizing maps (SOM). The autoencoders extract compact, latent representations of the recorded signals that are then clustered by the SOM. Results and Discussion: Our results, obtained on two datasets composed of in silico generated ONT-like signals, show that UNPLEX represents a promising starting point for the development of effective tools to cluster the signals corresponding to the same cell.

Application of Fabric Phase Sorptive Extraction (FPSE) Engaged to Tandem LC-MS/MS for Quantification of Brorphine in Oral Fluid.

Brorphine (1-[1-[1-(4-bromophenyl) ethyl]-piperidin-4-yl]-1,3-dihydro-2H-benzo [d]imidazol-2-one) is one of the most recent novel synthetic opioids (NSOs) on the novel psychoactive substances (NPSs) market, involved in over 100 deaths in 2020. Brorphine is a substituted piperidine-benzimidazolone analogue that retains structural similarities to fentanyl, acting as a full agonist at the µ-opioid receptor. Oral Fluid (OF) is an alternative matrix, frequently analyzed for the detection of NPS. Fabric phase sorptive extraction (FPSE) is a superior, green-sample -preparation technology recently applied for drug analysis. This contribution presents the development and validation of a method, based on the application of FPSE and liquid chromatography-tandem mass spectrometry (LC-MS/MS), to determine/quantitate brorphine in OF. The method's linearity ranged between 0.05 and 50 ng/mL (R2 = 0.9993), the bias ranged between 12.0 and 16.8%, and inter- and intra-day precisions ranged between 6.4 and 9.9%. Accuracy and extraction efficiency lied between 65 and 75%. LOD/LOQ were 0.015 ng/mL/0.05 ng/mL. Analyte's post-preparative stability was higher than 95%, while no matrix interferences and carryover between runs were observed. This is the first report introducing the application of FPSE for NPS determination, specifically, the quantification of brorphine in OF, thereby presenting a simple, rapid, sensitive, specific, effective, and reliable procedure engaged to LC-MS/MS that is suitable for routine application and the analysis of more NPSs.

An Experimental and Computational Protocol to Study Cell Proliferation in Human Acute Myeloid Leukemia Xenografts.

Acute myeloid leukemia (AML) is a highly frequent hematological malignancy, characterized by clinical and biological diversity, along with high relapse and mortality rates. The inherent functional and genetic intra-tumor heterogeneity in AML is thought to play an important role in disease recurrence and resistance to chemotherapy. Patient-derived xenograft (PDX) models preserve important features of the original tumor, allowing, at the same time, experimental manipulation and in vivo amplification of the human cells. Here we present a detailed protocol for the generation of fluorescently labeled AML PDX models to monitor cell proliferation kinetics in vivo, at the single-cell level. Although experimental protocols for cell proliferation studies are well established and widespread, they are not easily applicable to in vivo contexts, and the analysis of related time-series data is often complex to achieve. To overcome these limitations, model-driven approaches can be exploited to investigate different aspects of cell population dynamics. Among the existing approaches, the ProCell framework is able to perform detailed and accurate stochastic simulations of cell proliferation, relying on flow cytometry data. In particular, by providing an initial and a target fluorescence histogram, ProCell automatically assesses the validity of any user-defined scenario of intra-tumor heterogeneity, that is, it is able to infer the proportion of various cell subpopulations (including quiescent cells) and the division interval of proliferating cells. Here we explain the protocol in detail, providing a description of our methodology for the conditional expression of H2B-GFP in human AML xenografts, data processing by flow cytometry, and the final elaboration in ProCell.

cuProCell: GPU-Accelerated Analysis of Cell Proliferation With Flow Cytometry Data.

The investigation of cell proliferation can provide useful insights for the comprehension of cancer progression, resistance to chemotherapy and relapse. To this aim, computational methods and experimental measurements based on in vivo label-retaining assays can be coupled to explore the dynamic behavior of tumoral cells. ProCell is a software that exploits flow cytometry data to model and simulate the kinetics of fluorescence loss that is due to stochastic events of cell division. Since the rate of cell division is not known, ProCell embeds a calibration process that might require thousands of stochastic simulations to properly infer the parameterization of cell proliferation models. To mitigate the high computational costs, in this paper we introduce a parallel implementation of ProCell's simulation algorithm, named cuProCell, which leverages Graphics Processing Units (GPUs). Dynamic Parallelism was used to efficiently manage the cell duplication events, in a radically different way with respect to common computing architectures. We present the advantages of cuProCell for the analysis of different models of cell proliferation in Acute Myeloid Leukemia (AML), using data collected from the spleen of human xenografts in mice. We show that, by exploiting GPUs, our method is able to not only automatically infer the models' parameterization, but it is also 237× faster than the sequential implementation. This study highlights the presence of a relevant percentage of quiescent and potentially chemoresistant cells in AML in vivo, and suggests that maintaining a dynamic equilibrium among the different proliferating cell populations might play an important role in disease progression.

Quantification of Self-renewal in Murine Mammosphere Cultures.

The mammary gland is characterized by extensive regeneration capacity, as it goes through massive hormonal changes throughout the life cycle of a female. The role of mammary stem cells (MaSCs) is widely studied both in the physiological/developmental context and with regards to breast carcinogenesis. In this aspect, ex vivo studies focused on MaSC properties are highly sought after. Mammosphere cultures represent a surrogate of organ formation and have become a valuable tool for both basic and translational research. Here, we present a detailed protocol for the generation of murine primary mammosphere cultures and the quantitation of MaSC growth properties. The protocol includes mammary gland collection and digestion, isolation of primary mammary epithelial cells (MECs), establishment of primary mammosphere cultures, serial passaging, quantitation of mammosphere growth parameters and interpretation of the results. As an example, we present the effect of low-level constitutive Myc expression on normal MECs leading to increased self-renewal and proliferation.

p53 Loss in Breast Cancer Leads to Myc Activation, Increased Cell Plasticity, and Expression of a Mitotic Signature with Prognostic Value.

Loss of p53 function is invariably associated with cancer. Its role in tumor growth was recently linked to its effects on cancer stem cells (CSCs), although the underlying molecular mechanisms remain unknown. Here, we show that c-myc is a transcriptional target of p53 in mammary stem cells (MaSCs) and is activated in breast tumors as a consequence of p53 loss. Constitutive Myc expression in normal mammary cells leads to increased frequency of MaSC symmetric divisions, extended MaSC replicative-potential, and MaSC-reprogramming of progenitors, whereas Myc activation in breast cancer is necessary and sufficient to maintain the expanding pool of CSCs. Concomitant p53 loss and Myc activation trigger the expression of 189 mitotic genes, which identify patients at high risk of mortality and relapse, independently of other risk factors. Altogether, deregulation of the p53:Myc axis in mammary tumors increases CSC content and plasticity and is a critical determinant of tumor growth and clinical aggressiveness.