<![CDATA[Claridge-Chang Lab

<![CDATA[Claridge-Chang Lab - Blog]]>Wed, 10 Jun 2026 00:35:42 -0700Weebly<![CDATA[Mini-meta to summarize all internal results]]>Thu, 25 Jun 2026 07:00:00 GMThttps://www.claridgechang.net/blog/mini-meta-to-summarize-all-internal-resultsBy Yishan Mai

A difficult question I ran into early in my PhD was: When multiple experimenters do the same experiment but produce different results, what do you do?

Many would either cherry-pick the “best” replicate or blindly average results; the first conceals data while the second is statistically unsound. To solve this problem, we developed mini meta-analysis for DABEST 2.0, which lets you synthesize results from internally replicated experiments. It allows you to:

— Visualize effect sizes from each replicate
— Compute a weighted meta-analytic effect
— See the consistency (or heterogeneity) across your replicates

So next time you and your colleagues have the urge to argue on whose replicate is more “correct”, consider using mini meta-analysis to combine your data into a single, meaningful conclusion, while maintaining transparency in data reporting.

Preprint: https://doi.org/10.64898/2026.01.26.701654
Code: https://github.com/ACCLAB/DABEST-python

Work in collaboration with: Zinan Lu, Jonathan Anns, Sangyu Xu, Nicole Lee, Hyungwon Choi, Adam Claridge-Chang, and others.

[Also posted here.]

]]><![CDATA[Repeated measures: a better way]]>Thu, 21 May 2026 07:00:00 GMThttps://www.claridgechang.net/blog/repeated-measures-a-better-wayBy Jonathan Anns

Why your repeated-measures data deserves better than a simple line

Whenever the same subjects are measured more than once, whether across timepoints, doses, or conditions, you have a repeated-measures design. It's one of the most common frameworks in biomedical research. Yet research papers typically reduce these experiments into a mean line with error bars and P-values (Fig. 1A).

So, what's missing? The individual trajectories. The sense of variability. The actual magnitude of change.

In addition, the typical analysis approach entails a combinatorial explosion of post-hoc tests computing every possible pairwise comparison (Fig. 1B), many of which are not relevant to your hypothesis and unnecessarily inflate your multiple comparisons burden. The questions that actually motivated the study (when does the effect begin?, how large does it grow?, and does it persist?) are obfuscated.

Our new software, DABEST 2.0, is designed around a different approach: keep the individuals, and draw the overall shape. Our repeated-measures figure has two panels doing two distinct jobs. The upper panel shows observed values and their dispersion, an attribute of the sample (Fig. 1C). The lower panel shows the bootstrap distribution of the effect at each timepoint, an inference of precision that sharpens as the sample grows (Fig. 1D).

With DABEST 2.0, you can:
— Visualise each subject's individual trajectory alongside the means.
— Report the effect size with confidence intervals for the comparisons you care about.
— Show the full distribution of differences.

The result is a figure that more clearly quantifies (in a pretty way!) what changed, for whom, and by how much.

Preprint: https://doi.org/10.64898/2026.01.26.701654
Code: https://github.com/ACCLAB/DABEST-python

Work in collaboration with: Zinan Lu, Yishan Mai, Sangyu Xu, Nicole Lee, Hyungwon Choi, Adam Claridge-Chang, and others.

]]><![CDATA[Preprint: Long-Stokes-Shift mScarlet3 as a Structural Marker for Two-Photon Imaging]]>Thu, 14 May 2026 07:00:00 GMThttps://www.claridgechang.net/blog/preprint-long-stokes-shift-mscarlet3-as-a-structural-marker-for-two-photon-imagingExcited to share our new preprint! 🎉

We made a transgenic fly harbouring a long-Stokes-shift red fluorescent protein (LSSmScarlet3) that can be imaged alongside green GCaMP using a single 920 nm laser. No second laser needed.

This will let researchers do dual-channel functional and structural two-photon imaging more simply and affordably than before. We show the spectral properties, validate it in live fly (Drosophila) brains, and demonstrate minimal crosstalk with the green channel. In addition, we think this tool could be useful well beyond structural marking in applications like from co-imaging synaptic activity to tracking subcellular localization and protein levels in real time.

The transgenic line is validated and ready to use, we hope it's useful to the fly imaging community.

Preprint: https://doi.org/10.64898/2026.04.12.718060

Work by Sangyu Xu, Xianyuan Zhang, King Yee Cheung, Yishan Mai and others.

]]><![CDATA[Preprint: A mushroom-body output neuron that mediates octopamine-driven and hunger-motivated feeding in Drosophila]]>Wed, 13 May 2026 07:00:00 GMThttps://www.claridgechang.net/blog/preprint-a-mushroom-body-output-neuron-that-mediates-octopamine-driven-and-hunger-motivated-feeding-in-drosophila
What if a single brain cell could make you hungry or ruin your appetite?

That's essentially what we found in the vinegar fly (Drosophila). A neuron called MBON11 in the fly brain, can drive feeding behavior in both directions. Activate this cell with red light and flies eat more, even if they’re full. Silence this neuron with green light, and hungry flies eat much less.

Curiously, MBON11 is located in the mushroom body, which is the brain region flies use to learn and remember. We show that it also directly controls the drive to eat, drawing the picture that memory circuits are intimately involved in feeding decisions and, seemingly, hunger itself.

We found that two different chemical signals feed into this neuron. Adrenaline-like octopamine can increase feeding without being strictly necessary, while dopamine is required for hunger to translate into eating, but can't override a full stomach on its own. MBON11 receives both of these types of signals.

A methodological note: we used a feeding assay that directly measures consumption, and instead of relying on a single feeding metric, we used multi-dimensional ‘phenovectors’, and contextualized them against natural hunger and satiety states. This gave us a richer, holistic picture of how different circuit manipulations affect feeding patterns overall.

Proud of Xianyuan Zhang and the whole team on this one. 🎉

Preprint out now: https://doi.org/10.64898/2026.03.13.711740

Xianyuan Zhang, Sangyu Xu, Joses Ho, James C. Stewart

]]><![CDATA[Nicole Lee defends thesis]]>Wed, 15 Apr 2026 07:00:00 GMThttps://www.claridgechang.net/blog/nicole-lee-defends-thesisThrilled to announce the successful thesis defense of lab member and doctoral student Nicole Lee! She grew from strength to strength, and has written an excellent thesis on the relationship between motor functions and valence, and developing new optogenetic tools to better silence neuronal circuits.

The examiners posed some challenging questions that Nicole handled with aplomb. Shawn Je Hong-Wen Tang Caroline Wee Joshua Gooley

]]><![CDATA[Preprint: Getting over ANOVA]]>Fri, 13 Feb 2026 06:38:20 GMThttps://www.claridgechang.net/blog/preprint-getting-over-anovaHere's a dirty secret about ANOVA: it tests a null hypothesis that nobody cares about. When you run a one-way ANOVA, you're testing whether "all group means are equal." But even if you reject this hypothesis, you learn nothing about which groups differ, in which direction, or by how much.

So you embark on a second analytical step: multiple two-group comparisons. A modest six-group experiment suddenly requires testing 15 hypotheses. To manage this multiplicity, you apply corrections like Bonferroni, which undermine your statistical power. What you posed as a focused research question has sprawled into a complex web of subsidiary tests, forced by the ANOVA ritual.

Our new preprint, "Getting over ANOVA: Estimation graphics for multi-group comparisons," makes the case for a better approach. Estimation statistics encourages you to compare each test group to a single control, focusing on the effect sizes that actually matter. A six-group experiment focuses attention on just five effect sizes with confidence intervals, showing magnitude and precision directly.

The preprint introduces estimation methods for a range of multi-group designs: repeated-measures experiments, 2×2 factorial designs, binary outcome data, and mini-meta analysis for internal replicates. Each can replace data-analysis practices used in thousands of studies every year.

Read our new preprint here: https://doi.org/10.64898/2026.01.26.701654

Also posted on LinkedIn. #Statistics #OpenScience #DataVisualization #Research]]><![CDATA[The Word That Wasn't There]]>Mon, 26 Jan 2026 16:30:29 GMThttps://www.claridgechang.net/blog/the-word-that-wasnt-thereI was writing about serotonin-receiving neurons and reached for "serotonoceptive." The word should exist, but it doesn't.

We have "dopaminergic" for neurons that release dopamine, so why no equivalent for neurons that receive it? Instead, the literature is full of workarounds: "dopamine-sensitive neurons," "neurons expressing dopamine receptors," "dopamine target cells."

A solution was hiding in plain sight: "nociceptive" and "proprioceptive" have been around since Sherrington. Recent papers already use "GABAceptive" and "dopaminoceptive."

So I wrote a short paper proposing we generalize the '-ceptive' suffix. Dopaminergic neurons release dopamine; dopaminoceptive neurons receive it. Simple, systematic, and searchable.

Read the editorial here: https://doi.org/10.5281/zenodo.18373728]]><![CDATA[First Image of the Actin Nucleus: The Seed That Grows the Cytoskeleton]]>Mon, 08 Dec 2025 11:10:23 GMThttps://www.claridgechang.net/blog/first-image-of-the-actin-nucleus-the-seed-that-grows-the-cytoskeletonFor 50 years, biologists have known that cells build their internal scaffolding from actin filaments, but we've never actually seen how filament formation begins. I'm excited to share that our collaborative team has solved this basic mystery about the cytoskeleton.

Using x-ray crystallography, the Robinson group captured the first atomic-resolution structure of an actin nucleus: the three-molecule complex that starts every actin filament. Their secret weapon? Villin protein from Paralvinella sulfincola, a remarkable worm that thrives in scalding deep-sea thermal vents. Collected by submarine, the worm’s naturally stable actin-binding protein proved perfect for crystallization.

The three actin molecules in the nucleus aren't identical: each adopts a different shape, representing different stages of the transformation from individual units to filament building blocks. They also discovered a molecular gate that dynamically opens and closes to allow new actin molecules to join the growing filament.

The structure also illuminates how actin-binding proteins cut filaments: they exploit natural fluctuations to compete for binding sites and destabilize the structure. This principle likely applies to other actin-binding proteins relevant to disease and development, opening new avenues for intervention.

This work was led by the Robinson group, with contributions from the Girguis (marine biology) and Copley (genomics) groups.

Our paper is out now in Science Advances. https://doi.org/10.1126/sciadv.adw6915

]]><![CDATA[Using a long-Stokes-shift dye for two-photon microscopy]]>Thu, 04 Dec 2025 09:23:58 GMThttps://www.claridgechang.net/blog/using-a-long-stokes-shift-dye-for-two-photon-microscopyTwo colors from one laser: new preprint from my lab about a novel dye application.

Motion artifacts and anatomical orientation can pose challenges to two-photon live imaging. A second color channel helps with both problems—but usually requires a second expensive laser. We found another way. The dye ATTO 490LS is a long-Stokes-shift fluorescent dye that's been around for a decade, but its two-photon properties were unknown. We've now found that 490LS works beautifully with a 920 nm laser, the same wavelength used for GFP and GCaMP imaging. Excite with 920 nm, collect both green and red light with two detectors. One laser, two colors.

Having a stable red marker like 490LS lets you find a region of interest and distinguish real calcium transients from motion-induced changes. We're now working toward HaloTag and other conjugates for in vivo chemogenetic labeling, allowing calcium imaging with a stable reference. Please let us know if you're interested in trying some.

Preprint now on bioRxiv: https://www.biorxiv.org/content/10.1101/2025.11.21.689649v2.full

Work was led by King Yee Cheung, with help from Xianyuan Zhang , Danesha Devini Suresh, Masahiro Fukuda, and the NUS Microscopy Core.

]]><![CDATA[Genome of a thermal-vent worm yields insight into animal heat tolerance]]>Thu, 04 Dec 2025 09:20:48 GMThttps://www.claridgechang.net/blog/genome-of-a-thermal-vent-worm-yields-insight-into-animal-heat-toleranceHow do neurons keep working at the thermal limit of animal life?

Our new chromosome-scale genome of the Pompeii worm starts to answer. It has a conservative genome but a finely tuned proteome: expanded globins, anaerobic enzymes, and new sulfur chemistry. These let the worm thrive while grazing on bacteria in hot, dark, oxygen-starved vents at the bottom of the Pacific Ocean.

This new proteome now offers thermostable tools for biochemistry and a window into physiology at extremes.

This amazing project was led by Sami EL HILALI and Richard Copley, with contributions from the Robinson, Hoelz, Martín-Durán, and Jollivet groups.

Read the paper in BMC Biology https://doi.org/10.1186/s12915-025-02369-7

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