RESUMEN
Single-molecule localization microscopy (SMLM) has gained widespread use for visualizing the morphology of subcellular organelles and structures with nanoscale spatial resolution. However, analysis tools for automatically quantifying and classifying SMLM images have lagged behind. Here we introduce Enhanced Classification of Localized Point clouds by Shape Extraction (ECLiPSE), an automated machine learning analysis pipeline specifically designed to classify cellular structures captured through two-dimensional or three-dimensional SMLM. ECLiPSE leverages a comprehensive set of shape descriptors, the majority of which are directly extracted from the localizations to minimize bias during the characterization of individual structures. ECLiPSE has been validated using both unsupervised and supervised classification on datasets, including various cellular structures, achieving near-perfect accuracy. We apply two-dimensional ECLiPSE to classify morphologically distinct protein aggregates relevant for neurodegenerative diseases. Additionally, we employ three-dimensional ECLiPSE to identify relevant biological differences between healthy and depolarized mitochondria. ECLiPSE will enhance the way we study cellular structures across various biological contexts.
RESUMEN
We introduce a new automated machine learning analysis pipeline to precisely classify cellular structures captured through single molecule localization microscopy, which we call ECLiPSE (Enhanced Classification of Localized Pointclouds by Shape Extraction). ECLiPSE leverages 67 comprehensive shape descriptors encompassing geometric, boundary, skeleton and other properties, the majority of which are directly extracted from the localizations to accurately characterize individual structures. We validate ECLiPSE through unsupervised and supervised classification on a dataset featuring five distinct cellular structures, achieving exceptionally high classification accuracies nearing 100%. Moreover, we demonstrate the versatility of our approach by applying it to two novel biological applications: quantifying the clearance of tau protein aggregates, a critical marker for neurodegenerative diseases, and differentiating between two distinct morphological features (morphotypes) of TAR DNA-binding protein 43 proteinopathy, potentially associated to different TDP-43 strains, each exhibiting unique seeding and spreading properties. We anticipate that this versatile approach will significantly enhance the way we study cellular structures across various biological contexts, elucidating their roles in disease development and progression.
RESUMEN
Tau is a microtubule-associated protein, which promotes neuronal microtubule assembly and stability. Accumulation of tau into insoluble aggregates known as neurofibrillary tangles (NFTs) is a pathological hallmark of several neurodegenerative diseases. The current hypothesis is that small, soluble oligomeric tau species preceding NFT formation cause toxicity. However, thus far, visualizing the spatial distribution of tau monomers and oligomers inside cells under physiological or pathological conditions has not been possible. Here, using single-molecule localization microscopy, we show that tau forms small oligomers on microtubules ex vivo. These oligomers are distinct from those found in cells exhibiting tau aggregation and could be precursors of aggregated tau in pathology. Furthermore, using an unsupervised shape classification algorithm that we developed, we show that different tau phosphorylation states are associated with distinct tau aggregate species. Our work elucidates tau's nanoscale composition under nonaggregated and aggregated conditions ex vivo.
Asunto(s)
Microtúbulos/metabolismo , Neuronas/metabolismo , Proteínas tau/metabolismo , Humanos , Ovillos Neurofibrilares/metabolismo , Fosforilación , Imagen Individual de Molécula , Proteínas tau/genéticaAsunto(s)
Antineoplásicos/uso terapéutico , Carcinoma Epitelial de Ovario/genética , Epigénesis Genética , Neoplasias de Células Germinales y Embrionarias/genética , Neoplasias Ováricas/genética , Antineoplásicos/farmacología , Carcinoma Epitelial de Ovario/tratamiento farmacológico , Islas de CpG/genética , Metilación de ADN/efectos de los fármacos , Resistencia a Antineoplásicos/efectos de los fármacos , Resistencia a Antineoplásicos/genética , Femenino , Regulación Neoplásica de la Expresión Génica/efectos de los fármacos , Código de Histonas/efectos de los fármacos , Código de Histonas/genética , Humanos , Neoplasias de Células Germinales y Embrionarias/tratamiento farmacológico , Neoplasias Ováricas/tratamiento farmacológico , ARN no Traducido/genética , Transducción de Señal/efectos de los fármacos , Transducción de Señal/genética , Resultado del TratamientoRESUMEN
Triple negative breast cancer is a very aggressive subtype of breast cancer characterized by high recurrence rates and a greater likelihood of death compared to other breast cancers. Additionally, it is characterized by lack of expression of the estrogen and progesterone receptors and human epidermal growth factor receptor 2 (HER2)/neu. The current treatment for triple negative breast cancer is chemotherapy and that often results in a poor outcome. Therefore, it is essential that new, alternative therapeutic targets are identified. MicroRNAs are small non-coding elements that regulate the expression of various genes. Research has identified microRNAs promoting and in some cases suppressing cell proliferation by targeting genes in triple negative breast cancer cells. Thus, they are promising cancer targets and they should be further investigated as they could function as biomarkers of triple negative breast cancer in the future. Here we focus on the role of microRNAs in triple negative breast cancer and their potential as therapeutic targets.
Asunto(s)
Antineoplásicos/uso terapéutico , Regulación Neoplásica de la Expresión Génica , MicroARNs/genética , MicroARNs/uso terapéutico , Elementos Reguladores de la Transcripción , Neoplasias de la Mama Triple Negativas/genética , Biomarcadores de Tumor , Proliferación Celular , Femenino , Perfilación de la Expresión Génica , Silenciador del Gen , Humanos , Metástasis de la Neoplasia , Análisis de Secuencia por Matrices de Oligonucleótidos , Resultado del TratamientoRESUMEN
Lysosomes are organelles with an acidic lumen containing hydrolases, whose role is to break down macromolecules into their subunits. Cathepsins, which are lysosomal proteases, have important roles in cancer. Enhanced cathepsin activity out of the cell is linked to tumor growth, whereas such activity inside the cell is linked to tumor growth inhibition. Recently published studies on the opposing roles of cathepsin in cancer have investigated their potential as biochemical and nanotechnological therapeutic targets.
Asunto(s)
Catepsinas/antagonistas & inhibidores , Lisosomas/enzimología , Neoplasias/tratamiento farmacológico , Neoplasias/enzimología , Catepsinas/genética , Catepsinas/metabolismo , Muerte Celular/efectos de los fármacos , Inhibidores Enzimáticos/uso terapéutico , Humanos , Lisosomas/genética , Neoplasias/genética , Neoplasias/fisiopatologíaRESUMEN
Colorectal cancer (CRC) is the third most common type of cancer and is responsible for 9 % of cancer deaths in both men and women in the USA for 2013. It is a heterogenous disease, and its three classification types are microsatellite instability, chromosomal instability, and CpG island methylator phenotype. Biomarkers are molecules, which can be used as indicators of cancer. They have the potential to achieve great sensitivities and specificities in diagnosis and prognosis of CRC. DNA methylation biomarkers are epigenetic markers, more specifically genes that become silenced after aberrant methylation of their promoter in CRC. Some methylation biomarkers like SEPT9 (ColoVantage®) and vimentin (ColoSure(TM)) are already commercially available. Other blood and fecal-based biomarkers are currently under investigation and clinical studies so that they can be used in the near future. Biomarker panels are also currently being studied since they show great potential in diagnosis as they can combine robust biomarkers to achieve even greater sensitivities than single markers. Finally, methylation-sensitive microRNAs (miRNAs) are very promising markers, and their investigation as biomarkers, is only at primitive stage.