Figures And Figure Legends: Reading And Evaluating Science

Figures And Figure Legendsreading And Evaluating Science Is Often Abo

Figures and Figure Legends. Reading and evaluating science is often about looking at and interpreting figures and figure legends. What makes a good figure? What makes a good figure legend? When we go to read a figure legend what are we looking for? This assignment is about reading, and producing figure legends. Introduction: The parts of a good figure legend. The object of a good figure legend is to give your audience the overall conclusion of the figure, and all of the technical aspects that they need to understand what you are doing in that figure. However, this is a fairly short piece of writing. Thus, you are often required to assume a certain amount of knowledge from your audience. The purpose of talking about “variables” in our techniques section was partly to give you some knowledge of what you need to know in order to interpret figure legends. The labeling on the figure also plays a big role in your audience’s ability to understand and interpret your figure. Thus, unlabeled or poorly labeled figures are some of the worst elements of science. A well labeled figure and its legend should almost stand alone, and an experienced scientist can generally understand and evaluate the majority of a figure without having to read the associated text. We are going to practice this here, but first, what are the parts of a figure legend? Figure legends can be broken into 3 parts: 1) The big picture conclusion sentence: This sentence is designed to tell your reader what you found overall in the experiments depicted in the figure. 2) The experimental details, this gives your reader all of the variables they need to look at the figure and understand what you did (the labels on the figures are also very important for this). 3) Statistics - for quantitative figures, the number of “n’s” and the p values often go at the bottom of the figure legend. This assignment is broken into 3 parts. We will start by looking at a figure and its associated legend, and you will be asked to interpret what the figure is showing you, then you will be given a figure without a legend, and you will describe the results of the figure, and write your own figure legend, and finally I will ask you to draw your own figure with labels and figure legends. When you are drawing your own figure you can do this on the computer, or just draw on a piece of paper and scan it or take a picture of it and turn it in. If you are taking a picture please be sure any labels are legible in your image. The figures we are using here are from a paper that is a continuation of the experiments that we examined last week. This is partly to give you some background about basically what they are doing, so that we can look at these figures in isolation from the paper. In class on Friday, we will briefly look at these figures, and then we will go on and try to look at the figures from a completely unrelated paper, and see if we can understand and interpret those figures on their own. Although I am sure you could go find this paper, please don’t. The purpose of this is to practice looking at and evaluating figures without the associated words. Part 3 of this assignment mimics something that I might ask you to do on an exam. (Although in that case I often let you design your own experiment rather than giving you an experiment and results). Thus, you can think of this as exam practice. Here is a great example of a figure and legend (in this case I have removed A-C to make this a single experiment):

Figure 1: Depletion of Gαq/11 inhibits traffic-pulse-dependent SFKs activation at the Golgi complex. D) HeLa cells were treated with non-targeting siRNAs (Ctrl), siRNAs against Gαq/11 and Gαs (Gαq/11, Gαs siRNA) for 72 h, or 400 ng/ml PTX for 16 h. After infection with VSV for 45 min, the cells were incubated at 40°C for 3 h (temperature block) and then shifted to 32°C for 30 min (block release). Control cells and siRNAs-treated cells were fixed and stained for active SFKs (p-SFKs, grey scale) and giantin (marker for Golgi area definition). Merged images following the temperature-block release are shown (p-SFKs/Giantin; red and green respectively). Scale bars, 10 μm. (E) Quantification of data illustrated in (D). The p-SFKs IF intensities at the Golgi complex are expressed as arbitrary units (AU). Data are mean values (±s.d.) from four independent experiments. *Po0.001 compared with 32°C control (ANOVA analysis). (F) Western blotting reveals decreases in Gαq/11 and Gαs levels in siRNA-treated cells, compared with control cells. HeLa cells were treated with non-targeting siRNAs (Ctrl) and siRNAs against Gαq/11 and Gαs (siRNA) for 72 h, and then homogenized. The cell lysates were analysed by immunoblotting for the different Gα subunits, with actin as the loading control.

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Paper For Above instruction

Understanding scientific figures is a crucial component of scientific literacy, enabling researchers and students to interpret experimental data independently of accompanying texts. This skill involves analyzing figure components—including labels, legends, and visual data—to discern experimental outcomes and draw accurate conclusions. In this paper, we will explore the key parts of a well-constructed figure legend, interpret a specific figure example, and discuss strategies for designing clear, informative figures and legends, emphasizing the importance of clarity and self-sufficiency in scientific communication.

Parts of a Good Figure Legend

A well-crafted figure legend serves to succinctly summarize the overall findings of a figure, provide the necessary methodological details for understanding the data, and include statistical information where appropriate. It generally comprises three parts:

1. The Big Picture Conclusion Sentence: This introductory statement conveys the primary takeaway—the overall result or trend observed in the data. For example, it might state, "Depletion of Gαq/11 reduces SFKs activation at the Golgi complex," clearly summarizing the figure’s core finding.
2. Experimental Details: This section describes the experimental conditions, variables, labels, and treatments used. It clarifies what each component represents, enabling the reader to interpret the visual data correctly. For example, it might specify cell types, treatment durations, markers, and expected biological responses.
3. Statistics: Quantitative data are often supported by statistical analysis. The legend should specify the number of replicates (n), statistical tests used, and significance levels (p-values). For example, "Data are mean ± s.d. from four independent experiments; *p

Interpreting a Sample Figure

Consider a figure illustrating the effects of siRNA treatments on G-protein levels and SFKs activation in HeLa cells. In this context, the figure includes immunofluorescence images and Western blots. The images show staining for active SFKs and Golgi markers; the Western blots reveal protein expression levels. The legend states that HeLa cells were treated with non-targeting siRNA, siRNAs against Gαq/11 and Gαs, or pertussis toxin (PTX), followed by infection and temperature manipulation to study Golgi-associated signaling.

Interpreting such a figure involves examining the staining intensity at the Golgi (indicating SFKs activation), comparing protein levels across treatments (via Western blots), and understanding the role of each treatment. For example, siRNA against Gαq/11 and Gαs reduces their respective protein levels, as confirmed by Western blotting, which correlates with decreased SFKs activation at the Golgi, indicating a functional relationship between G-protein levels and SFKs activity.

Designing Clear Figures and Legends

Effective figures should be designed for clarity and ease of interpretation. Labels must be precise and unambiguous, including scale bars, color legends, and marker names. For Western blots, loading controls such as actin or tubulin are essential to verify equal protein loading across samples. When presenting multiple experiments, organizing images and data logically—such as grouping controls alongside treatments—helps avoid confusion.

To strengthen the interpretation, additional controls, such as using a non-specific siRNA or rescue experiments (restoring protein levels), can verify the specificity of siRNA effects. For example, if Gαs levels could be affected by Gαq/11 siRNA due to off-target effects, including a rescue experiment where Gαs is re-expressed would clarify the specific contribution of each protein.

Conclusion

In sum, a good figure and legend are indispensable for transparent scientific communication. They should allow the reader to understand the experimental design, interpret the data independently, and evaluate the validity of the conclusions. Developing skills in creating and analyzing such figures enhances scientific literacy and ensures that research findings are accessible and reproducible. As science advances, clear visual data presentation remains a cornerstone of effective dissemination and critical evaluation.

References

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