Background Shoot architecture is fundamentally crucial to crop growth and productivity. As a key component of shoot architecture, plant height is known to be controlled by both genetic and environmental factors, though specific details remain scarce. Results In this study, 308 representative soybean lines from a core collection and 168 F9 soybean progeny were planted at distinct field sites. The results demonstrated the presence of significant genotype × environment interaction (G × E) effects on traits associated with plant height in a natural soybean population. In total, 19 loci containing 51 QTLs (quantitative trait locus) for plant height were identified across four environments, with 23, 13 and 15 being QTLs for SH (shoot height), SNN (stem node number) and AIL (average internode length), respectively. Significant LOD ranging from 2.50 to 16.46 explained 2.80–26.10% of phenotypic variation. Intriguingly, only two loci, Loc11 and Loc19–1, containing 20 QTLs, were simultaneously detected across all environments. Results from Pearson correlation analysis and PCA (principal component analysis) revealed that each of the five agro-meteorological factors and four soil properties significantly affected soybean plant height traits, and that the corresponding QTLs had additive effects. Among significant environmental factors, AD (average day-length), AMaT (average maximum temperature), pH, and AN (available nitrogen) had the largest impacts on soybean plant height. Therefore, in spite of uncontrollable agro-meteorological factors, soybean shoot architecture might be remolded through combined efforts to produce superior soybean genetic materials while also optimizing soil properties. Conclusions Overall, the comprehensive set of relationships outlined herein among environment factors, soybean genotypes and QTLs in effects on plant height opens new avenues to explore in work aiming to increase soybean yield through improvements in shoot architecture.

Background:Shoot architecture is fundamentally crucial to crop growth and productivity. As a key component of shoot architecture, plant height is known to be controlled by both genetic and environmental factors, though specific details remain scarce.Results:In this study, 308 representative soybean lines from a core collection and 168 F9 soybean progeny were planted at distinct field sites. The results demonstrated the presence of significant genotype × environment interaction (G × E) effects on traits associated with plant height in a natural soybean population. In total, 19 loci containing 51 QTLs (quantitative trait locus) for plant height were identified across four environments, with 23, 13 and 15 being QTLs for SH (shoot height), SNN (stem node number) and AIL (average internode length), respectively. Significant LOD ranging from 2.50 to 16.46 explained 2.80% - 26.10% of phenotypic variation. Intriguingly, only two loci, Loc11 and Loc19-1, containing 20 QTLs, were simultaneously detected across all environments. Results from Pearson correlation analysis and PCA (principal component analysis) revealed that each of the five agro-meteorological factors and four soil properties significantly affected soybean plant height traits, and that the corresponding QTLs had additive effects. Among significant environmental factors, AD (average day-length), AMaT (average maximum temperature), pH, and AN (available nitrogen) had the largest impacts on soybean plant height. Therefore, in spite of uncontrollable agro-meteorological factors, soybean shoot architecture might be remolded through combined efforts to produce superior soybean genetic materials while also optimizing soil properties.Conclusions:Overall, the comprehensive set of relationships outlined herein among environment factors, soybean genotypes and QTLs in effects on plant height opens new avenues to explore in work aiming to increase soybean yield through improvements in shoot architecture.

Long-term multi-functional imaging and analysis of live cells require streamlined, functional coordination of various hardware and software platforms. However, manual control of various equipment produced by different manufacturers is labor-intensive and time-consuming, potentially decreasing the accuracy, reproducibility, and quality of acquired data. Therefore, an all-in-one and user-programmable system that enables automatic, multi-functional, and long-term image acquisition and is compatible with most fluorescent microscopy platforms can benefit the scientific community. This paper introduces the complete operating protocols of utilizing a novel integrated software system that consists of (1) a home-built software program, titled "Automatic Multi-functional Integration Program (AMFIP)," which enables automatic multi-channel imaging acquisition, and (2) a suite of quantitative imaging analysis and cell traction computation packages.This integrated system is applied to reveal the previously unknown relationship between the spatial-temporal distribution of mechano-sensitive Yes-associated protein (YAP) and the cell mechanics, including cell spreading and traction, in CRISPR/Cas9-engineered human normal cells (B2B) and lung cancer cells (PC9).Leveraging this system's capability of multi-channel control and readout, the result shows: (1) B2B normal cells and PC9 cancer cells show a distinct relationship between YAP expression, traction, and cell dynamics during cell spreading and migration processes; and (2) PC9 cancer cells apply noticeable peri-nuclear forces on substrates. In summary, this paper presents a detailed stepwise protocol on how to utilize an integrated user-programmable system that enables automatic multi-

Automatic operations of multi-functional and time-lapse live-cell imaging are necessary for the biomedical science community to study active, multi-faceted, and long-term biological phenomena. To achieve automatic control, most existing solutions often require the purchase of extra software programs and hardware that rely on the manufacturers’ own specifications. However, these software programs are usually non-user-programmable and unaffordable for many laboratories. To address this unmet need, we have developed a novel open-source software program, titled Automatic Multi-functional Integration Program (AMFIP), as a new Java-based and hardware-independent system that provides proven advantages over existing alternatives to the scientific community. Without extra hardware, AMFIP enables the functional synchronization of the μManager software platform, the Nikon NIS-Elements platform, and other 3rd party software to achieve automatic operations of most commercially available microscopy systems, including but not limited to those from Nikon. AMFIP provides a user-friendly and programmable graphical user interface (GUI), opening the door to expanding the customizability for myriad hardware and software systems according to user-specific experimental requirements and environments. To validate the intended purposes of developing AMFIP, we applied it to elucidate the question whether single cells, prior to their full spreading, can sense and respond to a soft solid substrate, and if so, how does the interaction depend on the cell spreading time and the stiffness of the substrate. Using a CRISPR/Cas9-engineered human epithelial Beas2B (B2B) cell line that expresses mNeonGreen2-tagged mechanosensitive Yes-associated protein (YAP), we show that single B2B cells develop distinct substrate-stiffness-dependent YAP expressions within 10 hours at most on the substrate, suggesting that cells are able to sense, distinguish, and respond to mechanical cues prior to the establishment of full cell spreading. In summary, AMFIP provides a reliable, open-source, and cost-free solution that has the validated long-term utility to satisfy the need of automatic imaging operations in the scientific community.

Automatic operations of multi-functional and time-lapse live-cell imaging are necessary for the biomedical science community to study active, multi-faceted, and long-term biological phenomena. To achieve automatic control, most existing solutions often require the purchase of extra software programs and hardware that rely on the manufacturers’ own specifications. However, these software programs are usually non-user-programmable and unaffordable for many laboratories. To address this unmet need, we have developed a novel open-source software program, titled Automatic Multi-functional Integration Program (AMFIP), as a new Java-based and hardware-independent system that provides proven advantages over existing alternatives to the scientific community. Without extra hardware, AMFIP enables the functional synchronization of the μManager software platform, the Nikon NIS-Elements platform, and other 3rd party software to achieve automatic operations of most commercially available microscopy systems, including but not limited to those from Nikon. AMFIP provides a user-friendly and programmable graphical user interface (GUI), opening the door to expanding the customizability for myriad hardware and software systems according to user-specific experimental requirements and environments. To validate the intended purposes of developing AMFIP, we applied it to elucidate the question whether single cells, prior to their full spreading, can sense and respond to a soft solid substrate, and if so, how does the interaction depend on the cell spreading time and the stiffness of the substrate. Using a CRISPR/Cas9-engineered human epithelial Beas2B (B2B) cell line that expresses mNeonGreen2-tagged mechanosensitive Yes-associated protein (YAP), we show that single B2B cells develop distinct substrate-stiffness-dependent YAP expressions within 10 hours at most on the substrate, suggesting that cells are able to sense, distinguish, and respond to mechanical cues prior to the establishment of full cell spreading. In summary, AMFIP provides a reliable, open-source, and cost-free solution that has the validated long-term utility to satisfy the need of automatic imaging operations in the scientific community.