Computer vision tools in PlantCV to create an accurate, flexible, and high-throughput method for microscopy image analysis of stomata

High-throughput microscopy image analysis of plant stomata

Murphy M., Harmon C., Allen D., Gehan M. (2022)

Katie Murphy, Courtney Harmon, Doug Allen, Malia Gehan,

Authorea November 01, 2022 – DOI: 10.22541/au.166733723.30480037/v1 –

https://www.authorea.com/doi/full/10.22541/au.166733723.30480037

Abstract

High-oil tobacco varieties have been recently engineered to produce increased leaf oil content for future food and fuel needs. An engineered variety of Nicotiana tabacum produces ~30 percent of leaf dry weight in lipids in the form of triacylglycerol (TAG), a significant increase relative to the less than 1 percent storage oil normally found in wild-type leaves. This high-oil tobacco also accumulates oil bodies in stomatal guard cells. In order to understand the impact of oil on guard cell shape, aperture, and dynamics, we have co-opted computer vision tools in PlantCV to create an accurate, flexible, and high-throughput method for microscopy image analysis of stomata. To this end, leaf impressions are made with silicone putty; clear nail polish peels of the putty impressions are imaged using light microscopy. Binary thresholding followed by point-and-click regions of interest and morphology calculations provide stomatal counts, aperture, and other shape characteristics. Applying this method to high-oil tobacco demonstrated reduced stomatal aperture but the same number of stomata per unit leaf area, providing a mechanistic explanation of high-oil tobacco responses to high temperature and water deficit stresses.

The traditional concept of Y-shaped white stomatal bands in Chamaecyparis obtusa should be revised to describe the arrangement of the aggregated waxy stomata that occur in rows

Electron microscopic observations of stomata, epicuticular waxes, and papillae in Chamaecyparis obtusa: Reconsidering the traditional concept of Y-shaped white stomatal bands

Kim K. W. (2018)

Ki Woo Kim,

Microsc Res Tech. 81: 716–723 – https://doi.org/10.1002/jemt.23027 – PMID: 29624793 –

https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/jemt.23027

Abstract

The foliar morphological characters of hinoki (Chamaecyparis obtusa) were revisited using optical and scanning electron microscopy. In C. obtusa, typical Y-shaped white stomatal bands were evident on the abaxial leaf surfaces. Two facial leaves and two lateral leaves were observed at the same node. Waxy papillae and oval stomata were arranged in two or three rows with protuberant rims on the abaxial leaf surfaces. Higher magnifications revealed the deposition of epicuticular waxes (tubules) on the Y-shaped white stomatal bands. Given the absence of stomatal bands after dewaxing with organic solvents, the white stomatal bands in C. obtusa were related to the epicuticular waxes rather than the presence of aggregated stomata alone. In contrast to C. obtusa, a single median leaf and two lateral leaves were observed at the same node of oriental arborvitae (Platycladus koraiensis). Neither stomatal bands nor papillae were observed on P. koraiensis leaves. The stomatal density and epicuticular waxes in the stomatal regions of C. obtusa were higher than those of P. koraiensis. This study suggests that the traditional concept of Y-shaped white stomatal bands in C. obtusa should be revised to describe the arrangement of the aggregated waxy stomata that occur in rows.

A method to obtain in vivo surface information of plant leaves and stomatal impressions by imaging replicas with SEM that is rapid and non-invasive

Fig 2. SEM micrographs showing surface structure of chemical fixed leaves and leaf replicas. (A) Overview of the surface of a leaf
and (B) close up of a stomatal complex after chemical fixation, critical point drying, and sputter coating under high vacuum conditions.
(C, E) Overview of the surface of a leaf and (D, F) close up of a stomatal complex from the replica. All images were taken from the
center of the leaf as indicated by the black box in Fig 1. While images C and D feature youngest fully developed leaves (Day 1), images
A, B, E and F feature adult leaves 14 days after the replica was taken (Day 14). Image C-F were taken from replicas taken from the exact
same leaf at the beginning of the experiment (Day 1) and 14 days after a replica was taken from that leaf (Day 14). Images A and B were
taken with the Versa 3D SEM at 30 kV while images C-F were taken with the TM-3030Plus at 15kV. Bars = 50 μm in A, C, and E, and
10 μm in B, D, and F.

Novel perspectives on stomatal impressions: Rapid and non-invasive surface characterization of plant leaves by scanning electron microscopy

Matthaeus W. J., Schmidt J., White J. D., Zechmann B. (2020)

William J. Matthaeus1, Jonathan Schmidt1, Joseph D. White1, Bernd Zechmann 2,

1 Department of Biology, Baylor University, Waco, Texas, United States of America,

2 Center for Microscopy and Imaging, Baylor University, Waco, Texas, United States of America

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PLoS ONE 15(9): e0238589 – https://doi.org/10.1371/journal.pone.0238589 –

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0238589&type=printable

A web-based interface is provided to apply the model of stomatal detection for any soybean data that makes use of the new clearing protocol

Optimizing the Experimental Method for Stomata-Profiling Automation of Soybean Leaves Based on Deep Learning

Sultana S. N., Park H.,Choi S. H., Jo,H., Song J. T., Lee, J.-D., Kang Y. J. (2021)

by 

Syada Nizer Sultana ^1,†,

Halim Park ^2,†,

Sung Hoon Choi ²,

Hyun Jo ¹,

Jong Tae Song ¹,

Jeong-Dong Lee ^1,3,* and

Yang Jae Kang ^2,4,*

¹ Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Korea

² Division of Bio & Medical Big Data Department (BK4 Program), Gyeongsang National University, Jinju 52828, Korea

³ Department of Integrative Biology, Kyungpook National University, Daegu 41566, Korea

⁴ Division of Life Science Department, Gyeongsang National University, Jinju 52828, Korea

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MDPI 10(12) – 2714 – https://doi.org/10.3390/plants10122714 –

https://www.mdpi.com/2223-7747/10/12/2714

Abstract

Stomatal observation and automatic stomatal detection are useful analyses of stomata for taxonomic, biological, physiological, and eco-physiological studies. We present a new clearing method for improved microscopic imaging of stomata in soybean followed by automated stomatal detection by deep learning. We tested eight clearing agent formulations based upon different ethanol and sodium hypochlorite (NaOCl) concentrations in order to improve the transparency in leaves. An optimal formulation—a 1:1 (v/v) mixture of 95% ethanol and NaOCl (6–14%)—produced better quality images of soybean stomata. Additionally, we evaluated fixatives and dehydrating agents and selected absolute ethanol for both fixation and dehydration. This is a good substitute for formaldehyde, which is more toxic to handle. Using imaging data from this clearing method, we developed an automatic stomatal detector using deep learning and improved a deep-learning algorithm that automatically analyzes stomata through an object detection model using YOLO. The YOLO deep-learning model successfully recognized stomata with high mAP (~0.99). A web-based interface is provided to apply the model of stomatal detection for any soybean data that makes use of the new clearing protocol.

Preparation of epidermal peels and stomatal guard cell protoplasts

Preparation of epidermal peels and guard cell protoplasts for cellular, electrophysiological, and -omics assays of guard cell function

Zhu M., Jeon B. W., Geng S., Yu Y., Balmant K., Chen S., Assmann S. M. (2016)

Mengmeng Zhu ¹, Byeong Wook Jeon ¹, Sisi Geng ², Yunqing Yu ¹, Kelly Balmant ², Sixue Chen ², Sarah M Assmann ³,

• ¹ Biology Department, Penn State University, 208 Mueller Laboratory, University Park, PA, 16802, USA.
• ² Plant Molecular and Cellular Biology Program, Department of Biology, Genetics Institute, University of Florida, 2033 Mowry Road, Gainesville, FL, 32610, USA.
• ³ Biology Department, Penn State University, 208 Mueller Laboratory, University Park, PA, 16802, USA.

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Methods Mol. Biol. 1363: 89–121 – doi: 10.1007/978-1-4939-3115-6_9 –

https://pubmed.ncbi.nlm.nih.gov/26577784/

Abstract

Bioassays are commonly used to study stomatal phenotypes. There are multiple options in the choice of plant materials and species used for observation of stomatal and guard cell responses in vivo. Here, detailed procedures for bioassays of stomatal responses to abscisic acid (ABA) in Arabidopsis thaliana are described, including ABA promotion of stomatal closure, ABA inhibition of stomatal opening, and ABA promotion of reaction oxygen species (ROS) production in guard cells. We also include an example of a stomatal bioassay for the guard cell CO2 response using guard cell-enriched epidermal peels from Brassica napus. Highly pure preparations of guard cell protoplasts can be produced, which are also suitable for studies on guard cell signaling, as well as for studies on guard cell ion transport. Small-scale and large-scale guard cell protoplast preparations are commonly used for electrophysiological and -omics studies, respectively. We provide a procedure for small-scale guard cell protoplasting from A. thaliana. Additionally, a general protocol for large-scale preparation of guard cell protoplasts, with specifications for three different species, A. thaliana, B. napus, and Vicia faba is also provided.

A portable and low-cost stomata phenotyping method that could accurately and dynamically measure the characteristic parameters of living stomata

Results of stomatal pore segmentation by our method for images from the literature by Li et al. (2019). (a) Without reflection removal. (b) Stomata with small opening degree. (c) Blurred stomata.

StomataScorer: a portable and high-throughput leaf stomata trait scorer combined with deep learning and an improved CV model

Liang X., Xu X., Wang Z., He L., Zhang K, Liang B,, Ye J., Shi J., Wu X., Dai M., Yang W. (2022)

Xiuying Liang, Xichen Xu, Zhiwei Wang, Lei He, Kaiqi Zhang, Bo Liang, Junli Ye, Jiawei Shi, Xi Wu, Mingqiu Dai, Wanneng Yang,

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Plant Biotechnology Journal. 20(3): 577–591 – https://doi.org/10.1111/pbi.13741 –

https://onlinelibrary.wiley.com/doi/full/10.1111/pbi.13741

Abstract

To measure stomatal traits automatically and nondestructively, a new method for detecting stomata and extracting stomatal traits was proposed. Two portable microscopes with different resolutions (TipScope with a 40× lens attached to a smartphone and ProScope HR2 with a 400× lens) are used to acquire images of living stomata in maize leaves. FPN model was used to detect stomata in the TipScope images and measure the stomata number and stomatal density. Faster RCNN model was used to detect opening and closing stomata in the ProScope HR2 images, and the number of opening and closing stomata was measured. An improved CV model was used to segment pores of opening stomata, and a total of 6 pore traits were measured. Compared to manual measurements, the square of the correlation coefficient (R²) of the 6 pore traits was higher than 0.85, and the mean absolute percentage error (MAPE) of these traits was 0.02%–6.34%. The dynamic stomata changes between wild-type B73 and mutant Zmfab1a were explored under drought and re-watering condition. The results showed that Zmfab1a had a higher resilience than B73 on leaf stomata. In addition, the proposed method was tested to measure the leaf stomatal traits of other nine species. In conclusion, a portable and low-cost stomata phenotyping method that could accurately and dynamically measure the characteristic parameters of living stomata was developed. An open-access and user-friendly web portal was also developed which has the potential to be used in the stomata phenotyping of large populations in the future.

Rapid non-destructive imaging of leaf surfaces with automated image analysis

Stomata detection using the machine-learning model in wheat (a), rice (b), tomato (c) and Arabidopsis (d) images using the 400x magnification. The model detects and labels stomata with bounding boxes and gives the confidence number in each box. Scale bar = 0.1 mm.

Rapid non-destructive method to phenotype stomatal traits

Pathoumthong P. , Zhen Zhang Z., Roy S., El Habti A. (2022)

Phetdalaphone Pathoumthong, Zhen Zhang,  Stuart Roy,  Abdeljalil El Habti,

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bioRxiv – https://doi.org/10.1101/2022.06.28.497692 –

https://www.biorxiv.org/content/10.1101/2022.06.28.497692v1.full

Abstract

Background Stomata are tiny pores located on the leaf surface that are central to gas exchange. Stomatal number, size and aperture are key determinants of plant transpiration and photosynthesis, and any variation in these traits can affect plant growth and productivity. Current methods to screen for stomatal phenotypes are tedious, which impedes research on stomatal physiology and hinders efforts to develop resilient crops with optimised stomatal patterning. We developed a rapid non-destructive method to phenotype stomatal traits in four species: wheat, rice, tomato, and Arabidopsis.

Results The method consists of two steps. The first step is to capture images of a leaf surface directly and non-destructively using a handheld microscope, which only takes a few seconds compared to minutes using other methods. This rapid method also provides higher quality images for automated data analysis. The second step is to analyse stomatal features using a machine-learning model that automatically detects, counts stomata and measures size. The accuracy of the machine-learning model in detecting stomata ranged from 89% to 96%, depending on the species.

Conclusions We developed a method that combines rapid non-destructive imaging of leaf surfaces with automated image analysis. The method provides accurate data on stomatal features while significantly reducing time for data acquisition. It can be readily used to phenotype stomata in large populations in the field and in controlled environments.

An aspirated diffusion porometer suitable for field measurement

An aspirated diffusion porometer

Byrne G. F., Rose C. W., Slatyer R. O. (1970)

G.F.Byrne¹, C.W.Rose¹, R.O.Slatyer²,

¹Division of Land Research, C.S.I.R.O., Canberra, A.C.T. Australia

²Research School of Biological Sciences, Australian National University, Canberra, A.C.T. Australia

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Agricultural Meteorology 7: 39-44 – ISSN 0002-1571 – https://doi.org/10.1016/0002-1571(70)90005-1 –

https://www.sciencedirect.com/science/article/pii/0002157170900051

Abstract:

An aspirated diffusion porometer suitable for field measurement of leaf diffusive resistance, on leaves of small dimensions if so desired, is described. The forced circulation of air through the instrument cavity eliminates the errors due to thermal convection which have been a problem with instruments using molecular diffusion as the transport mechanism; it also significantly reduces instrument response time. The calibration and measurement procedures are described and illustrated.

Hematoxylin and Safranin for Staining Plant Materials

Delafield’S Hematoxylin and Safranin for Staining Plant Materials

Dean H. L. (2009)

Stain Technology 15(2): –

https://www.tandfonline.com/doi/abs/10.3109/10520294009110650

Abstract

An improved schedule is suggested for staining plant materials in Delafield’s hematoxylin and safranin. Tissues are stained first in Delafield’s hematoxylin. A short bath in acidulated water (1 or 2 drops concentrated HCl to 100 cc.) removes objectionable precipitates, and at the same time serves as a destaining agent. The acid bath must be followed quickly by a thoro wash in tap water, or dilute lithium carbonate solution, to restore the original dark blue color (made reddish in the acid bath) of the hematoxylin and to “set” the stain. Once the hematoxylin solution is satisfactory, none of the reagents ordinarily used will remove it—unless they contain acid. Tissues are counterstained in rapid safranin (5 drops analin in 100 cc. of 1% safranin 0 in 50% ethyl alcohol); this materially lessens the time necessary for staining. The safranin is de-stained in 50% ethyl alcohol (which does not affect the hematoxylin) until sharp differentiation is secured. If destaining is too slow, or differentiation poor, a quick rinse in acidulated 50% alcohol usually sharpens contrast of the stains. This must be followed quickly by a wash in 50% alcohol containing lithium carbonate to neutralize the acid. Dehydrate, and mount as usual. This schedule allows each stain to be individually, and independently, controlled at the will of the operator.

Photo’s of stomata

Microscopy of Nature

Vossen R. (xxxx)

https://microscopyofnature.com/stomata

Introduction

Stomata are small openings that mainly occur on the underside of leaves. They are surrounded by specialised cells and they regulate the gas exchange between the plant and it’s environment, the plant is ‘breathing’ through them, as it were. Stomata are very recognizable by the two kidney- or bean-shaped guard cells that regulate the size of the opening. The guard cells are specialised epidermal cells which contain vacuoles that change their shape when water is absorbed due to a process called turgor, causing the stomata to open. The stomata are opened by stimuli like high humidity and bright light. Depending on the plant family, guard cells are often surrounded by so-called subsidiary cells.

As for the morphology of stomata, some different shapes can be distinguished:

● anomocytic: without subsidiary cells

● paracytic: with lateral subsidiary cells oriented parallel with the guard cells

● tetracytic: with both lateral and polar subsidiary cells

Stomata are fascinating objects to study, in each plant they look a bit different or are positioned differently. To observe stomata we need to peel off the epidermis from the underside of a leaf. If you tear a leaf apart, often a small piece of the epidermis will come off. Especially with thicker leaves this works quite well. Easy to begin with are the leaves of Hosta, Prunus laurocerasus (Cherry laurel) and Tradescantia.

(Continued)