Abstract:The function and fate of cells is influenced by many
different
factors, one of which is surface topography of the support culture
substrate. Systematic studies of nanotopography and cell response
have typically been limited to single cell types and a small set of
topographical variations. Here, we show a radical expansion of experimental
throughput using automated detection, measurement, and classification
of co-cultured cells on a nanopillar array where feature height changes
continuously from planar to 250 n… Show more
“…A description of the way in which a number of these measurements are calculated is included in the supplementary section. These measurements can be generated using immunofluorescent data from many different cell types 46, 55 .Figure 4Immunofluorescent staining of monocytes and macrophages. ( A ) Monocytes cultured for 2 h; ( B ) monocytes cultured for 6 days; ( C ) naïve macrophages; ( D ) M1 macrophages; ( E ) M2 macrophages.…”
Section: Resultsmentioning
confidence: 99%
“…Methods applying such high content image analysis to the detection and classification of various cell types have been demonstrated, including in mesenchymal stem cells 45 and endothelial/fibroblast cells 46 . Beyond this, machine learning methods have also been used to classify the specific stage of the cell cycle, e.g.…”
Macrophages play a crucial rule in orchestrating immune responses against pathogens and foreign materials. Macrophages have remarkable plasticity in response to environmental cues and are able to acquire a spectrum of activation status, best exemplified by pro-inflammatory (M1) and anti-inflammatory (M2) phenotypes at the two ends of the spectrum. Characterisation of M1 and M2 subsets is usually carried out by quantification of multiple cell surface markers, transcription factors and cytokine profiles. These approaches are time-consuming, require large numbers of cells and are resource intensive. In this study, we used machine learning algorithms to develop a simple and fast imaging-based approach that enables automated identification of different macrophage functional phenotypes using their cell size and morphology. Fluorescent microscopy was used to assess cell morphology of different cell types which were stained for nucleus and actin distribution using DAPI and phalloidin respectively. By only analysing their morphology we were able to identify M1 and M2 phenotypes effectively and could distinguish them from naïve macrophages and monocytes with an average accuracy of 90%. Thus we suggest high-content and automated image analysis can be used for fast phenotyping of functionally diverse cell populations with reasonable accuracy and without the need for using multiple markers.
“…A description of the way in which a number of these measurements are calculated is included in the supplementary section. These measurements can be generated using immunofluorescent data from many different cell types 46, 55 .Figure 4Immunofluorescent staining of monocytes and macrophages. ( A ) Monocytes cultured for 2 h; ( B ) monocytes cultured for 6 days; ( C ) naïve macrophages; ( D ) M1 macrophages; ( E ) M2 macrophages.…”
Section: Resultsmentioning
confidence: 99%
“…Methods applying such high content image analysis to the detection and classification of various cell types have been demonstrated, including in mesenchymal stem cells 45 and endothelial/fibroblast cells 46 . Beyond this, machine learning methods have also been used to classify the specific stage of the cell cycle, e.g.…”
Macrophages play a crucial rule in orchestrating immune responses against pathogens and foreign materials. Macrophages have remarkable plasticity in response to environmental cues and are able to acquire a spectrum of activation status, best exemplified by pro-inflammatory (M1) and anti-inflammatory (M2) phenotypes at the two ends of the spectrum. Characterisation of M1 and M2 subsets is usually carried out by quantification of multiple cell surface markers, transcription factors and cytokine profiles. These approaches are time-consuming, require large numbers of cells and are resource intensive. In this study, we used machine learning algorithms to develop a simple and fast imaging-based approach that enables automated identification of different macrophage functional phenotypes using their cell size and morphology. Fluorescent microscopy was used to assess cell morphology of different cell types which were stained for nucleus and actin distribution using DAPI and phalloidin respectively. By only analysing their morphology we were able to identify M1 and M2 phenotypes effectively and could distinguish them from naïve macrophages and monocytes with an average accuracy of 90%. Thus we suggest high-content and automated image analysis can be used for fast phenotyping of functionally diverse cell populations with reasonable accuracy and without the need for using multiple markers.
“…Both the surface chemistry and topography can play a major role in the behaviour of biological materials [29]. As described above, ATR FTIR spectroscopy is a surface layer technique where the absorbance is measured only from the portion of a sample in direct contact with the ATR surface rather than from the bulk.…”
Section: Atr Ftir Spectroscopy Of Surfaces and Proteinsmentioning
Attenuated Total Reflection (ATR) Fourier Transform Infrared (FTIR) spectroscopy is a label-free, non-destructive analytical technique that can be used extensively to study a wide variety of different molecules in a range of different conditions. The aim of this review is to discuss and highlight the recent advances in the applications of ATR FTIR spectroscopic imaging to proteins. It briefly covers the basic principles of ATR FTIR spectroscopy and ATR FTIR spectroscopic imaging as well as their advantages to the study of proteins compared to other techniques and other forms of FTIR spectroscopy. It will then go on to examine the advances that have been made within the field over the last several years, particularly the use of ATR FTIR spectroscopy for the understanding and development of protein interaction with surfaces. Additionally, the growing potential of Surface Enhanced Infrared spectroscopy (SEIRAS) within this area of applications will be discussed. The review includes the applications of ATR FTIR imaging to protein crystallization and for highthroughput studies, highlighting the future potential of the technology within the field of protein structural studies and beyond.
“…The particular nanostructure used in this work is a regular array of nanoscale dots. This choice is motivated by two factors: firstly, this type of structure is known to be relevant for cell adhesion and differentiation experiments . Secondly, using single‐pixel exposure during electron beam lithography, the required exposure time is drastically reduced, and a large area can be nanopatterned in a relatively short time.…”
Injection moulding is a proven technology for high-throughput production of nanostructures, but high quality replication of pillar-type structures is a considerable challenge, due to the complexities of cavity filling at the timescales involved. We have developed a platform to systematically study the effects of nanostructures with aspect ratios up to 1.2:1 on the quality of moulding, and also considered options for polymer and tooling material. A master template containing nanostructures with a continuous variation in height is produced by a novel fabrication approach using a plasma polymerized hexane layer, deposited with a gradient in thickness, as a sacrificial etch mask. Injection moulding results show that process parameters (tooling temperature and cooling time), material (polystyrene and polycarbonate) as well as a tool surface coating can control the stretching of nanopillar replica dimensions, allowing a variety of final pattern heights using a single master.-2 -
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