Dataset Information

A fully automated pipeline for the dynamic at-line morphology analysis of microscale Aspergillus cultivation.

ABSTRACT:

Background

Morphology, being one of the key factors influencing productivity of filamentous fungi, is of great interest during bioprocess development. With increasing demand of high-throughput phenotyping technologies for fungi due to the emergence of novel time-efficient genetic engineering technologies, workflows for automated liquid handling combined with high-throughput morphology analysis have to be developed.

Results

In this study, a protocol allowing for 48 parallel microbioreactor cultivations of Aspergillus carbonarius with non-invasive online signals of backscatter and dissolved oxygen was established. To handle the increased cultivation throughput, the utilized microbioreactor is integrated into a liquid handling platform. During cultivation of filamentous fungi, cell suspensions result in either viscous broths or form pellets with varying size throughout the process. Therefore, tailor-made liquid handling parameters such as aspiration/dispense height, velocity and mixing steps were optimized and validated. Development and utilization of a novel injection station enabled a workflow, where biomass samples are automatically transferred into a flow through chamber fixed under a light microscope. In combination with an automated image analysis concept, this enabled an automated morphology analysis pipeline. The workflow was tested in a first application study, where the projected biomass area was determined at two different cultivation temperatures and compared to the microbioreactor online signals.

Conclusions

A novel and robust workflow starting from microbioreactor cultivation, automated sample harvest and processing via liquid handling robots up to automated morphology analysis was developed. This protocol enables the determination of projected biomass areas for filamentous fungi in an automated and high-throughput manner. This measurement of morphology can be applied to describe overall pellet size distribution and heterogeneity.

SUBMITTER: Jansen R

PROVIDER: S-EPMC7937226 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A fully automated pipeline for the dynamic at-line morphology analysis of microscale Aspergillus cultivation.

Jansen Roman R Küsters Kira K Morschett Holger H Wiechert Wolfgang W Oldiges Marco M

Fungal biology and biotechnology 20210306 1

<h4>Background</h4>Morphology, being one of the key factors influencing productivity of filamentous fungi, is of great interest during bioprocess development. With increasing demand of high-throughput phenotyping technologies for fungi due to the emergence of novel time-efficient genetic engineering technologies, workflows for automated liquid handling combined with high-throughput morphology analysis have to be developed.<h4>Results</h4>In this study, a protocol allowing for 48 parallel microbi ...[more]

PMID: 33676585

Similar Datasets

Project description:Accurate volume measurements of the brain structures are important for treatment evaluation and disease follow-up in multiple sclerosis (MS) patients. With the aim of obtaining reproducible measurements and avoiding the intra-/inter-rater variability that manual delineations introduce, several automated brain structure segmentation strategies have been proposed in recent years. However, most of these strategies tend to be affected by the abnormal MS lesion intensities, which corrupt the structure segmentation result. To address this problem, we recently reformulated two label fusion strategies of the state of the art, improving their segmentation performance on the lesion areas. Here, we integrate these reformulated strategies in a completely automated pipeline that includes pre-processing (inhomogeneity correction and intensity normalization), atlas selection, masked registration and label fusion, and combine them with an automated lesion segmentation method of the state of the art. We study the effect of automating the lesion mask acquisition on the structure segmentation result, analyzing the output of the proposed pipeline when used in combination with manually and automatically segmented lesion masks. We further analyze the effect of those masks on the segmentation result of the original label fusion strategies when combined with the well-established pre-processing step of lesion filling. The experiments performed show that, when the original methods are used to segment the lesion-filled images, significant structure volume differences are observed in a comparison between manually and automatically segmented lesion masks. The results indicate a mean volume decrease of 1.13%±1.93 in the cerebrospinal fluid, and a mean volume increase of 0.13%±0.14 and 0.05%±0.08 in the cerebral white matter and cerebellar gray matter, respectively. On the other hand, no significant volume differences were found when the proposed automated pipeline was used for segmentation, which demonstrates its robustness against variations in the lesion mask used.

Dataset Information

A fully automated pipeline for the dynamic at-line morphology analysis of microscale Aspergillus cultivation.

Background

Results

Conclusions

Publications

A fully automated pipeline for the dynamic at-line morphology analysis of microscale Aspergillus cultivation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets