Background Accurate quantification of mesenchymal stem cell proliferation and adipogenic differentiation is essential for applications ranging from metabolic disease research to cultivated fat production. However, conventional endpoint assays fail to capture the dynamics of these processes. Label-free live-cell imaging, combined with automated image analysis, enables continuous, non-invasive quantification over time. Methods Here, we present a comparative analysis of a device-integrated live-cell imaging analysis pipeline and custom-trained Cellpose segmentation models for label-free quantification of proliferation and adipogenic differentiation in human and porcine mesenchymal stem cells (MSCs). Results Both approaches enabled robust determination of cell number and confluence during proliferation. The device-integrated analysis showed higher accuracy at high confluence, whereas the custom-trained model achieved reliable single-cell segmentation at low to intermediate densities. During adipogenic differentiation, both methods successfully quantified adipocytes but differed in their segmentation strategies. The device-integrated analysis primarily detected contrast-rich regions associated with mature adipocytes, while the model segmented individual adipocytes, enabling quantification of both adipocyte area and number and allowing earlier detection of differentiation events. Custom-trained models demonstrated adaptability across species and imaging modalities, including manually acquired brightfield images. Conclusions Overall, label-free image analysis reduces experimental effort, avoids staining-related artefacts, and enables time-resolved characterization of cellular processes. While device-integrated analysis offers ease of use and seamless integration, Cellpose models provide superior flexibility and applicability, highlighting their complementary strengths for advanced cell culture analysis. All developed Cellpose models are publicly available.

Chronic antibody-mediated rejection (AMR) is a key limiting factor for the clinical outcome of a kidney transplantation (Ktx), where early diagnosis and therapeutic intervention is needed. This study describes the identification of the biomarker CXC-motif chemokine ligand (CXCL) 9 as an indicator for AMR and presents a new aptamer-antibody-hybrid lateral flow assay (hybrid-LFA) for detection in urine. Biomarker evaluation included two independent cohorts of kidney transplant recipients (KTRs) from a protocol biopsy program and used subgroup comparisons according to BANFF-classifications. Plasma, urine and biopsy lysate samples were analyzed with a Luminex-based multiplex assay. The CXCL9-specific hybrid-LFA was developed based upon a specific rat antibody immobilized on a nitrocellulose-membrane and the coupling of a CXCL9-binding aptamer to gold nanoparticles. LFA performance was assessed according to receiver operating characteristic (ROC) analysis. Among 15 high-scored biomarkers according to a neural network analysis, significantly higher levels of CXCL9 were found in plasma and urine and biopsy lysates of KTRs with biopsy-proven AMR. The newly developed hybrid-LFA reached a sensitivity and specificity of 71% and an AUC of 0.79 for CXCL9. This point-of-care-test (POCT) improves early diagnosis-making in AMR after Ktx, especially in KTRs with undetermined status of donor-specific HLA-antibodies.

Chemie Ingenieur TechnikVolume 94, Issue 9 p. 1319-1319 Poster Development of automated image processing algorithms for Bacillus spp. endospore detection L. Niemeyer, Corresponding Author L. Niemeyer niemeyer@iftc.uni-hannover.de Leibniz University Hannover, Institute of Technical Chemistry, Callinstr. 3–9, 30167 Hannover, GermanyCorrespondence: L. Niemeyer (niemeyer@iftc.uni-hannover.de), Leibniz University Hannover, Institute of Technical Chemistry, Callinstr. 3–9, 30167 Hannover, GermanySearch for more papers by this authorR. Biermann, R. Biermann Leibniz University Hannover, Institute of Technical Chemistry, Callinstr. 3–9, 30167 Hannover, GermanySearch for more papers by this authorI. Bice, I. Bice Biochem Zusatzstoffe Handels- und Produktionsgesellschaft mbH, Institute of Technical Chemistry, Küstermeyerstr. 16, 49393 Lohne, GermanySearch for more papers by this authorP. Lindner, P. Lindner Leibniz University Hannover, Institute of Technical Chemistry, Callinstr. 3–9, 30167 Hannover, GermanySearch for more papers by this authorS. Beutel, S. Beutel Leibniz University Hannover, Institute of Technical Chemistry, Callinstr. 3–9, 30167 Hannover, GermanySearch for more papers by this author L. Niemeyer, Corresponding Author L. Niemeyer niemeyer@iftc.uni-hannover.de Leibniz University Hannover, Institute of Technical Chemistry, Callinstr. 3–9, 30167 Hannover, GermanyCorrespondence: L. Niemeyer (niemeyer@iftc.uni-hannover.de), Leibniz University Hannover, Institute of Technical Chemistry, Callinstr. 3–9, 30167 Hannover, GermanySearch for more papers by this authorR. Biermann, R. Biermann Leibniz University Hannover, Institute of Technical Chemistry, Callinstr. 3–9, 30167 Hannover, GermanySearch for more papers by this authorI. Bice, I. Bice Biochem Zusatzstoffe Handels- und Produktionsgesellschaft mbH, Institute of Technical Chemistry, Küstermeyerstr. 16, 49393 Lohne, GermanySearch for more papers by this authorP. Lindner, P. Lindner Leibniz University Hannover, Institute of Technical Chemistry, Callinstr. 3–9, 30167 Hannover, GermanySearch for more papers by this authorS. Beutel, S. Beutel Leibniz University Hannover, Institute of Technical Chemistry, Callinstr. 3–9, 30167 Hannover, GermanySearch for more papers by this author First published: 25 August 2022 https://doi.org/10.1002/cite.202255076AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume94, Issue9Special Issue: (Bio)Process Engineering – a Key to Sustainable Development: ProcessNet and DECHEMA-BioTechNet Jahrestagungen 2022 together with 13th ESBES SymposiumSeptember 2022Pages 1319-1319 RelatedInformation

This work presents enabling technologies for the optimization of the manufacturing of GelMA-based hydrogels constructs with desired stiffness gradients. The manufacturing technique combines dynamic mixing for gradient generation and a passive micromixer for efficient hydrogel blending. A digital replica of the fabrication process is developed, integrating theoretical and computational models, as well as experimental data, in order to predict and control the stiffness profile obtained within the constructs. The workflow for the development of the in silico framework, based on rigorous verification, validation, and uncertainty quantification steps, is presented. The validation of the digital replica is based on reference settings of process variables, which result in constructs with an exponential stiffness profile. The developed in silico model has been employed for optimizing process variables in order to obtain a linear stiffness profile in the extruded construct without the need of expensive and time-consuming trial-and-error procedures. The developed digital replica is now a powerful tool for the creation of hydrogel gradient constructs for tissue engineering applications or for the screening of optimal 3D cell culture conditions.

Kidney is the most frequently transplanted among all solid organs worldwide. Kidney transplant recipients (KTRs) undergo regular follow-up examinations for the early detection of acute rejections. The gold standard for proving a T-cell mediated rejection (TCMR) is a biopsy of the renal graft often occurring as indication biopsy, in parallel to an increased serum creatinine that may indicate deterioration of renal transplant function. The goal of the current work was to establish a lateral flow assay (LFA) for diagnosing acute TCMR to avoid harmful, invasive biopsies. Soluble interleukin-2 (IL-2) receptor (sIl-2R) is a potential biomarker representing the α-subunit of the IL-2 receptor produced by activated T-cells, e.g., after allogen contact. To explore the diagnostic potential of sIL-2R as a biomarker for TCMR and borderline TCMR, plasma and urine samples were collected from three independent KTR cohorts with various distinct histopathological diagnostic findings according to BANFF (containing 112 rsp. 71 rsp. 61 KTRs). Samples were analyzed by a Luminex-based multiplex technique and cut off-ranges were determined. An LFA was established with two specific sIL-2R-antibodies immobilized on a nitrocellulose membrane. A significant association between TCMR, borderline TCMR and sIL-2R in plasma and between TCMR and sIL-2R in urine of KTRs was confirmed using the Mann-Whitney U test. The LFA was tested with sIL-2R-spiked buffer samples establishing a detection limit of 25 pM. The performance of the new LFA was confirmed by analyzing urine samples of the 2nd and 3rd patient cohort with 35 KTRs with biopsy proven TCMRs, 3 KTRs diagnosed with borderline TCMR, 1 mixed AMR/TCMR rsp. AMR/borderline TCMR and 13 control patients with a rejection-free kidney graft proven by protocol biopsies. The new point-of-care assay showed a specificity of 84.6% and sensitivity of 87.5%, and a superior estimated glomerular filtration rate (eGFR) at the time point of biopsy (specificity 30.8%, sensitivity 85%).

Bacillus spp. endospores are important dormant cell forms and are distributed widely in environmental samples. While these endospores can have important industrial value (e.g. use in animal feed as probiotics), they can also be pathogenic for humans and animals, emphasizing the need for effective endospore detection. Standard spore detection by colony forming units (CFU) is time-consuming, elaborate and prone to error. Manual spore detection by spore count in cell counting chambers via phase-contrast microscopy is less time-consuming. However, it requires a trained person to conduct. Thus, the development of a facilitated spore detection tool is necessary. This work presents two alternative quantification methods: first, a colorimetric assay for detecting the biomarker dipicolinic acid (DPA) adapted to modern needs and applied for Bacillus spp. and second, a model-based automated spore detection algorithm for spore count in phase-contrast microscopic pictures. This automated spore count tool advances manual spore detection in cell counting chambers, and does not require human overview after sample preparation. In conclusion, this developed model detected various Bacillus spp. endospores with a correctness of 85-89%, and allows an automation and time-saving of Bacillus endospore detection. In the laboratory routine, endospore detection and counting was achieved within 5-10 min, compared to up to 48 h with conventional methods. The DPA-assay on the other hand enabled very accurate spore detection by simple colorimetric measurement and can thus be applied as a reference method.

In this report, a fully integrated solution for laboratory digitization is presented. The approach presents a flexible and complete integration method for the digitally assisted workflow. The worker in the laboratory performs procedures in direct interaction with the digitized infrastructure that guides through the process and aids while performing tasks. The digital transformation of the laboratory starts with standardized integration of both new and “smart” lab devices, as well as legacy devices through a hardware gateway module. The open source Standardization in Lab Automation 2 standard is used for device communication. A central lab server channels all device communication and keeps a database record of every measurement, task and result generated or used in the lab. It acts as a central entry point for process management. This backbone enables a process control system to guide the worker through the lab process and provide additional assistance, like results of automated calculations or safety information. The description of the infrastructure and architecture is followed by a practical example on how to implement a digitized workflow. This approach is highly useful for – but not limited to – the biotechnological laboratory and has the potential to increase productivity in both industry and research for example by enabling automated documentation.

In this article a gateway module to integrate legacy laboratory devices into the network of the digital laboratory in the 21st century is introduced. The device is based on ready to buy consumer hardware that is easy to get and inexpensive. Depending on the specific requirements of the desired application (bare embedded computer, RS232 serial port connector, IP65 certified casing and connectors) the needed investment ranges from about 95 € up to 200 €. The embedded computer runs an open source Linux operating system and can in principle be used to run any kind of software needed for communicating with the laboratory device. Here the open source SiLA2 standard is used for presenting the device's functions in the network. As an example the digital integration of a magnetic stirrer is shown and can be used as a template for other applications. A method for easy remote integration of the device to ensure an easy and consistent workflow in development, testing and usage is also presented. This incorporates a method for remote installation of SiLA2 servers on the box as well as a web frontend for administration, debugging and management of those.

Background Despite the significant contribution of transcriptomics to the fields of biological and biomedical research, interpreting long lists of significantly differentially expressed genes remains a challenging step in the analysis process. Gene set enrichment analysis is a standard approach for summarizing differentially expressed genes into pathways or other gene groupings. Here, we explore an alternative approach to utilizing gene sets from curated databases. We examine the method of deriving custom gene sets which may be relevant to a given experiment using reference data sets from previous transcriptomics studies. We call these data-derived gene sets, "gene signatures" for the biological process tested in the previous study. We focus on the feasibility of this approach in analyzing immune-related processes, which are complicated in their nature but play an important role in the medical research. Results We evaluate several statistical approaches to detecting the activity of a gene signature in a target data set. We compare the performance of the data-derived gene signature approach with comparable GO term gene sets across all of the statistical tests. A total of 61 differential expression comparisons generated from 26 transcriptome experiments were included in the analysis. These experiments covered eight immunological processes in eight types of leukocytes. The data-derived signatures were used to detect the presence of immunological processes in the test data with modest accuracy (AUC = 0.67). The performance for GO and literature based gene sets was worse (AUC = 0.59). Both approaches were plagued by poor specificity. Conclusions When investigators seek to test specific hypotheses, the data-derived signature approach can perform as well, if not better than standard gene-set based approaches for immunological signatures. Furthermore, the data-derived signatures can be generated in the cases that well-defined gene sets are lacking from pathway databases and also offer the opportunity for defining signatures in a cell-type specific manner. However, neither the data-derived signatures nor standard gene-sets can be demonstrated to reliably provide negative predictions for negative cases. We conclude that the data-derived signature approach is a useful and sometimes necessary tool, but analysts should be weary of false positives.

Chemie Ingenieur TechnikVolume 92, Issue 9 p. 1249-1249 Poster Digital image analysis in the lab of the future M. Porr, M. Porr Leibniz Universität Hannover, Institut für Technische Chemie, Callinstr. 5, 30167 Hannover, GermanySearch for more papers by this authorD. Marquard, D. Marquard Leibniz Universität Hannover, Institut für Technische Chemie, Callinstr. 5, 30167 Hannover, GermanySearch for more papers by this authorF. Lange, F. Lange Leibniz Universität Hannover, Institut für Technische Chemie, Callinstr. 5, 30167 Hannover, GermanySearch for more papers by this authorJ. Austerjost, J. Austerjost Leibniz Universität Hannover, Institut für Technische Chemie, Callinstr. 5, 30167 Hannover, GermanySearch for more papers by this authorK. McVean, K. McVean Noack Laboratorien GmbH, Käthe-Paulus-Str. 1, 31157 Sarstedt, GermanySearch for more papers by this authorA. Herrmann, A. Herrmann Noack Laboratorien GmbH, Käthe-Paulus-Str. 1, 31157 Sarstedt, GermanySearch for more papers by this authorC. Maeß, C. Maeß Noack Laboratorien GmbH, Käthe-Paulus-Str. 1, 31157 Sarstedt, GermanySearch for more papers by this authorP. Lindner, P. Lindner Leibniz Universität Hannover, Institut für Technische Chemie, Callinstr. 5, 30167 Hannover, GermanySearch for more papers by this authorT. Scheper, T. Scheper Leibniz Universität Hannover, Institut für Technische Chemie, Callinstr. 5, 30167 Hannover, GermanySearch for more papers by this authorS. Beutel, Corresponding Author S. Beutel beutel@iftc.uni-hannover.de Leibniz Universität Hannover, Institut für Technische Chemie, Callinstr. 5, 30167 Hannover, GermanyCorrespondence: S. Beutel (beutel@iftc.uni-hannover.de), Leibniz Universität Hannover, Institut für Technische Chemie, Callinstr. 5, 30167 Hannover, GermanySearch for more papers by this author M. Porr, M. Porr Leibniz Universität Hannover, Institut für Technische Chemie, Callinstr. 5, 30167 Hannover, GermanySearch for more papers by this authorD. Marquard, D. Marquard Leibniz Universität Hannover, Institut für Technische Chemie, Callinstr. 5, 30167 Hannover, GermanySearch for more papers by this authorF. Lange, F. Lange Leibniz Universität Hannover, Institut für Technische Chemie, Callinstr. 5, 30167 Hannover, GermanySearch for more papers by this authorJ. Austerjost, J. Austerjost Leibniz Universität Hannover, Institut für Technische Chemie, Callinstr. 5, 30167 Hannover, GermanySearch for more papers by this authorK. McVean, K. McVean Noack Laboratorien GmbH, Käthe-Paulus-Str. 1, 31157 Sarstedt, GermanySearch for more papers by this authorA. Herrmann, A. Herrmann Noack Laboratorien GmbH, Käthe-Paulus-Str. 1, 31157 Sarstedt, GermanySearch for more papers by this authorC. Maeß, C. Maeß Noack Laboratorien GmbH, Käthe-Paulus-Str. 1, 31157 Sarstedt, GermanySearch for more papers by this authorP. Lindner, P. Lindner Leibniz Universität Hannover, Institut für Technische Chemie, Callinstr. 5, 30167 Hannover, GermanySearch for more papers by this authorT. Scheper, T. Scheper Leibniz Universität Hannover, Institut für Technische Chemie, Callinstr. 5, 30167 Hannover, GermanySearch for more papers by this authorS. Beutel, Corresponding Author S. Beutel beutel@iftc.uni-hannover.de Leibniz Universität Hannover, Institut für Technische Chemie, Callinstr. 5, 30167 Hannover, GermanyCorrespondence: S. Beutel (beutel@iftc.uni-hannover.de), Leibniz Universität Hannover, Institut für Technische Chemie, Callinstr. 5, 30167 Hannover, GermanySearch for more papers by this author First published: 28 August 2020 https://doi.org/10.1002/cite.202055184AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume92, Issue9Special Issue: 10. ProcessNet-Jahrestagung und 34. DECHEMA-Jahrestagung der Biotechnologen 2020: Processes for FutureSeptember 2020Pages 1249-1249 RelatedInformation

Lung surfactants are used for reducing alveolar surface tension in preterm infants to ease breathing. Phospholipid films with surfactant proteins regulate the activity of alveolar macrophages and reduce inflammation. Aberrant skin wound healing is characterized by persistent inflammation. The aim of the study was to investigate if lung surfactant can promote wound healing. Preclinical wound models, e.g. cell scratch assays and full-thickness excisional wounds in mice, and a randomized, phase I clinical trial in healthy human volunteers using a suction blister model were used to study the effect of the commercially available bovine lung surfactant on skin wound repair. Lung surfactant increased migration of keratinocytes in a concentration-dependent manner with no effect on fibroblasts. Significantly reduced expression levels were found for pro-inflammatory and pro-fibrotic genes in murine wounds. Because of these beneficial effects in preclinical experiments, a clinical phase I study was initiated to monitor safety and tolerability of surfactant when applied topically onto human wounds and normal skin. No adverse effects were observed. Subepidermal wounds healed significantly faster with surfactant compared to control. Our study provides lung surfactant as a strong candidate for innovative treatment of chronic skin wounds and as additive for treatment of burn wounds to reduce inflammation and prevent excessive scarring.

AbstractDas smartLAB ist ein Verbund aus akademischen und nicht‐akademischen Partnern mit dem Ziel eine realistische Vision des Labors der Zukunft zu entwickeln. Hierfür wird eine digitale und interaktive Laborumgebung geschaffen, die den Menschen im Laboralltag anleitet und unterstützt, nicht ersetzt. In diesem Artikel werden dabei die Gebiete Geräteansteuerung, Workflow‐Entwicklung, Dokumentation und Nutzerinteraktion beleuchtet sowie das Zusammenspiel dieser Bereiche. Außerdem wird die hardwareseitige Umsetzung dargestellt und wichtige Konzepte erläutert.

Microbial contamination in mammalian cell cultures causing rejected batches is costly and highly unwanted. Most methods for detecting a contamination are time-consuming and require extensive off-line sampling. To circumvent these efforts and provide a more convenient alternative, we used an online in situ microscope to estimate the cell diameter of the cellular species in the culture to distinguish mammalian cells from microbial cells depending on their size. A warning system was set up to alert the operator if microbial cells were present in the culture. Hybridoma cells were cultured and infected with either Candida utilis or Pichia stipitis as contaminant. The warning system could successfully detect the introduced contamination and alert the operator. The results suggest that in situ microscopy could be used as an efficient online tool for early detection of contaminations in cell cultures.

Standard operating procedures (SOPs) are an often-used medium for the reproducible step-by-step execution of complex laboratory operations. Even in today's era of digitalization, most SOPs are still paper-based. This might cause disorder within the lab and often distracts the experimenter from the actual experiment. Furthermore, most of the experimental documentation nowadays is still done manually by transferring experimental data and results in paper-based laboratory journals, which is quite sensitive to errors as well as time-consuming. We developed a digital laboratory infrastructure that enables interactive hands-free experiment guidance and instrument control via smart safety goggles, also called smartglasses. Instructions for an SOP and experimental data can be displayed directly into the experimenter's field of view and triggered by voice commands. Experimental data and working steps can be processed and documented instantly. The developed laboratory infrastructure allows for the flexible integration of laboratory instruments and SOPs. Different SOPs commonly used for the spectrophotometric analysis of beer (MEBAK methods) were implemented showing the applicability of the established system in the brewing laboratory. Furthermore, a benchmark study of the system was performed. The developed solution enables a faster experiment execution and analysis than conventional paper-based SOPs and is a first step towards the integration of smart safety goggles for the support of laboratory workflows. This might pave the way to easier process documentation and an increase of laboratory workflow efficiency.

Chemie Ingenieur TechnikVolume 91, Issue 3 p. 180-183 InhaltFree Access Inhalt: Chem. Ing. Tech. 3/2019 First published: 21 February 2019 https://doi.org/10.1002/cite.201970033AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume91, Issue3Special Issue: Digitalisierung in Forschung und EntwicklungMarch 2019Pages 180-183 RelatedInformation

Fluorescence spectroscopy is a highly sensitive and non-invasive technique for the identification of characteristic process states and for the online monitoring of substrate and product concentrations. Nevertheless, fluorescence sensors are mainly used in academic studies and are not well implemented for monitoring of industrial production processes. In this work, we present a newly developed robust online fluorescence sensor that facilitates the analysis of fluorescence measurements. The set-up of the sensor was miniaturised and realised without any moveable part to be robust enough for application in technical environments. It was constructed to measure only the three most important biologic fluorophores (tryptophan, NADH and FAD/FMN), resulting in a significant data reduction compared to conventional a 2-D fluorescence spectrometer. The sensor performance was evaluated by calibration curves and selectivity tests. The measuring ranges were determined as 0.5–50 µmol L−1 for NADH and 0.0025–7.5 µmol L−1 for BSA and riboflavin. Online monitoring of batch cultivations of wild-type Escherichia coli K1 in a 10 L bioreactor scale were performed. The data sets were analysed using principal component analysis and partial least square regression. The recorded fluorescence data were successfully used to predict the biomass of an independent cultivation (RMSEP 4.6 %).

The introduction of smart virtual assistants (VAs) and corresponding smart devices brought a new degree of freedom to our everyday lives. Voice-controlled and Internet-connected devices allow intuitive device controlling and monitoring from all around the globe and define a new era of human–machine interaction. Although VAs are especially successful in home automation, they also show great potential as artificial intelligence-driven laboratory assistants. Possible applications include stepwise reading of standard operating procedures (SOPs) and recipes, recitation of chemical substance or reaction parameters to a control, and readout of laboratory devices and sensors. In this study, we present a retrofitting approach to make standard laboratory instruments part of the Internet of Things (IoT). We established a voice user interface (VUI) for controlling those devices and reading out specific device data. A benchmark of the established infrastructure showed a high mean accuracy (95% ± 3.62) of speech command recognition and reveals high potential for future applications of a VUI within the laboratory. Our approach shows the general applicability of commercially available VAs as laboratory assistants and might be of special interest to researchers with physical impairments or low vision. The developed solution enables a hands-free device control, which is a crucial advantage within the daily laboratory routine.

The manual counting of colonies on agar plates to estimate the number of viable organisms (so-called colony-forming units-CFUs) in a defined sample is a commonly used method in microbiological laboratories. The automation of this arduous and time-consuming process through benchtop devices with integrated image processing capability addresses the need for faster and higher sample throughput and more accuracy. While benchtop colony counter solutions are often bulky and expensive, we investigated a cost-effective way to automate the colony counting process with smart devices using their inbuilt camera features and a server-based image processing algorithm. The performance of the developed solution is compared to a commercially available smartphone colony counter app and the manual counts of two scientists trained in biological experiments. The comparisons show a high accuracy of the presented system and demonstrate the potential of smart devices to displace well-established laboratory equipment.

In situ Microscopy (ISM) is an optical non-invasive technique to monitor cells in bioprocesses in real-time. Escherichia coli is the most studied and used organism in biotechnology. In this article the cell density in Escherichia coli cultivations was monitored by applying ISM in these cultivations. The acquired images were analyzed with an image processing algorithm to determine the turbidity of the cultivation medium. In three cultivations the cell density was monitored with the algorithm and offline samples were taken to determine the dry cell mass (DCM). Both results were correlated and concentrations up to 70 g/L DCM could be measured via ISM. For higher cell densities a saturation was recognized. The deviation of the calibration lines within three cultivations was 8%.

Desoxyribonucleic acid (DNA) microarray experiments generate big datasets. To successfully harness the potential information within, multiple filtering, normalization, and analysis methods need to be applied. An in-depth knowledge of underlying physical, chemical, and statistical processes is crucial to the success of this analysis. However, due to the interdisciplinarity of DNA microarray applications and experimenter backgrounds, the published analyses differ greatly, for example, in methodology. This severely limits the comprehensibility and comparability among studies and research fields. In this work, we present a novel end-user software, developed to automatically filter, normalize, and analyze two-channel microarray experiment data. It enables the user to analyze single chip, dye-swap, and loop experiments with an extended dynamic intensity range using a multiscan approach. Furthermore, to our knowledge, this is the first analysis software solution, that can account for photobleaching, automatically detected by an artificial neural network. The user gets feedback on the effectiveness of each applied normalization regarding bias minimization. Standardized methods for expression analysis are included as well as the possibility to export the results in the Gene Expression Omnibus (GEO) format. This software was designed to simplify the microarray analysis process and help the experimenter to make educated decisions about the analysis process to contribute to reproducibility and comparability.