Archive for the ‘cell’ tag

Feeling Clumpy?

By March 20th, 2014 at 9:07 am | Comment

Citizen scientists can help ID the progression of bacterial infection in plant cells by determining how “clumpy” plant cell images are.

Explore the microbiome around and inside you with these citizen science projects!

The language on the Clumpy homepage might be considered a challenge for the average citizen scientist: “The model plant-pathogen system comprising the plant Arabidopsis thaliana and the pathogenic bacterium Pseudomonas syringae has been used very effectively to elucidate the nature of the pathogenic interaction.” However, once you get started on this citizen science project, you will soon get a feel for it, and perhaps even enjoy it as I did.

Clumpy is a citizen science project that tests for a bacterial infection in plants. When microbiologists found that organelles in the plant’s photosynthetic cells (i.e. chloroplasts) tended to “clump” together when subjected to bacterial infection, they saw opportunity for a citizen science experiment whereby the public could assist with help in classifying images for ‘clumpiness.’

The Clumpy project came out of observations made by Dr. Littlejohn, a postdoctoral researcher in the Bioscience Department at the University of Exeter; Dr. Murray Grant, a Professor of Plant Molecular Biology; and Dr. John Love, Associate Professor in Plant and Industrial Biotechnology at the University of Exeter. They noticed this unusual phenomenon, which might have important implications for how they understand plant-pathogen interactions. “It was through a conversation with Professor Richard Everson at an Exeter Imaging Network meeting, that the multidisciplinary team got together to set up the online experiment,” said Littlejohn.

The idea to use citizen science to annotate bacterial infections in plants came about in part due to the difficulty in using computational methods alone. Trying to characterize images using abstract notions such as ‘clumpiness’ is an area where humans can easily outperform current computational approaches. In addition, annotation of the images doesn’t require any special expertise, so the problem seemed like an ideal match for citizen science. “We also thought the images themselves were intrinsically interesting, which would help motivate people to provide a wide range of annotations,” said Hugo Hutt, a PhD student in the Department of Physics at the University of Exeter.

Dr. Littlejohn commented, “It would be great to know if it were the plant or the bacterium that initiates the ‘clumping’ of chloroplasts in the leaves. This might help us understand why the chloroplasts clump and which partner in the pathosystem is benefiting from it.” What does this do for society, you might ask? The benefit of a study like this might include fighting diseases in food crops for one.

Verifying the Data

When I first started to use the Clumpy website to classify whether an image was clumpy or not clumpy, I kept second guessing myself, and moved through the selections very deliberately, mulling each one over carefully. They all looked too similar, which got me to thinking about verification of the data. Since all science depends on accurate data, how do they know whether or not answers provided by the average Joe are accurate or not?

Hutt was involved with this aspect of Clumpy—the verification of data:

In 2013 we published an article in Computational Intelligence about this. We tested statistical methods to evaluate the accuracy and reliability of users. For example, to evaluate the accuracy we compared the user annotations with those assigned by an expert. We also measured the degree of consensus among users based on how correlated their annotations were.

The results showed a surprising level of accuracy, which my purely objective test might support—after about 15 attempts I began to recognize ‘clumpiness’ more intuitively, and after just 30 I felt I had it nailed. Hutt and colleagues hope to publish an extended version of the paper this year in a special issue of Soft Computing.


With respect to obtaining a consensus score people were asked to make annotations in one of three paradigms: classification, scoring and ranking. Termed “a web-based citizen science experiment,” Clumpy tasks are evaluated in relation to the accuracy and agreement among the participants using both simulated and real-world data from the experiment. The results show a clear difference in performance between the three tasks, with the ranking task obtaining the highest accuracy and agreement among the participants. That means people like me were capable of producing accurate results when we checked in to Clumpy!

[Note: Read more on how other projects verify crowdsourced data through consensus, and read about one success story about how crowdsourced data was comparable to that produced by leading experts.]

Until now the results have been used to publish more on the computer-collection side, than on the plant biology side, but Littlejohn said, “We are looking to fit this result in with a broader biological study.”

The project has been running since August 2012 and currently houses over 10,000 annotated images. Imagine the cost, time and resources allocated to this process, if citizen scientists had not been involved!

Images: Courtesy of

Ian Vorster has a MS in Environmental Communications and most recently served as director of communications at the Woods Hole Research Center in Massachusetts. Prior to that he worked in the health communications field. Ian has served as a designer, writer, photographer, editor and project leader in the field of science, and now works freelance in a blend of these roles. You can see more of Ian’s work at

Cancer Research in the Classroom – Accelerating Cures with the Click of a Mouse

By August 23rd, 2013 at 10:41 am | Comment

This project is part of our Back to School 2013 round-up of projects. Read more about them!

CellBreast cancer is the single most common cancer in women worldwide with roughly 1 in 8 women developing the disease each year. Chances are, a friend or family member is coping with this diagnosis right now. Following Angelina Jolie’s announcement earlier this year about her family’s struggle with breast cancer and her treatment choices, advances in biomedical research and personalized medicine increasingly hold the promise of a day when cancer is cured. How do scientists find the clues buried within tumor samples?

Cell Slider, a collaboration between Cancer Research UK and Zooniverse, is the first citizen scientist project whose goal is to speed up cancer research by enlisting citizen scientists to analyze real tumor samples. According to Professor Andrew Handby, a CRUK scientist from the University of Leeds who helped develop Cell Slider, “Computers can only go so far – they can pick up obvious trends but only the human eye can spot subtleties that have, in the past, lead to important serendipitous discoveries… Cell Slider makes our data accessible – it’s not just for scientists and computer geeks – everyone can play their part in curing cancer.”

Identify normal blood and tissue cells as well as irregular cancer cells in Cell Slider.

Identify normal blood and tissue cells as well as irregular cancer cells in Cell Slider.

Ideal for secondary school science classes, Cell Slider is a real-life citizen scientist project that uses the same methods researchers use everyday in the laboratory to identify cancer cells. Students are introduced to some of the common core principles in life sciences, including basic cell types and shapes, while developing analytical and critical thinking skills. You don’t have to be a scientist to participate in this project; simple mouse clicks help researchers around the world find new cancer treatments buried in simple tumor samples.

During a brief tutorial, students are introduced to the three cell types typically seen on the microscope slides (white blood cells, tissue cells, and cancer cells), taught to identify normal and cancer cells based on shape and staining, then asked to analyze real images of breast cancer tumors. A special yellow dye that sticks to oestrogen receptor (ER) helps identify cells with excessive ER and candidates for cancer treatments using hormonal therapies such as tamoxifen. Once the irregularly shaped, yellow-stained, cancer cells are identified you estimate their number and how strongly they are stained through a matching game. Using this data, researchers are beginning to understand the connections between molecules found on cancer cells and the effects of common treatments on the outcome of the disease.

“Eventually, we hope to be able to identify different types of breast, and other, cancers and find out how these different types respond to different treatments,” said Professor Paul Pharoah, a CRUK scientist from Cambridge University who helped develop Cell Slider. “This will enable us to match up women with the right cancer drugs based on their tumor type. We hope that this personalized medicine approach would be a reality in years to come, but this computer program could make it a reality sooner than any of us had imagined possible.”

Since its launch in October 2012, more than 860,000 citizen scientists from around the world have analyzed over 1.7 million images. Could we be just your mouse click away from a cure?

Photo : Cell Slider

Dr Melinda T. Hough is a freelance science advocate and writer.  Her previous work has included a Mirzayan Science and Technology Graduate Policy Fellowship at the National Academy of Sciences (2012), co-development of several of the final science policy questions with (2012), consulting on the development of the Seattle Science Festival EXPO day (2012), contributing photographer for JF Derry’s book “Darwin in Scotland” (2010) and outreach projects to numerous to count.  Not content to stay stateside, Melinda received a B.S in Microbiology from the University of Washington (2001) before moving to Edinburgh, Scotland where she received a MSc (2002) and PhD (2008) from the University of Edinburgh trying to understand how antibiotics kill bacteria.  Naturally curious, it is hard to tear Melinda away from science; but if you can, she might be found exploring, often behind the lens of her Nikon D80, training for two half-marathons, or plotting her next epic adventure.