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NEUROTRAILBLAZERS · TEACHING MATERIALS

EM Prep and Imaging

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Contents · 17 slides
  1. 1. EM Prep and Imaging
  2. 2. Session outcomes (60 minutes)
  3. 3. Pedagogical arc
  4. 4. Acquisition chain (operational)
  5. 5. Before EM: what a light microscope could draw
  6. 6. Automated against manual, on the same tissue
  7. 7. Real data: one section of human cortex
  8. 8. Real data: the resolution you must reach everywhere
  9. 9. Artifact taxonomy with downstream impact
  10. 10. QA gates before full ingest
  11. 11. Pilot-first strategy
  12. 12. Throughput vs fidelity tradeoff
  13. 13. Misconceptions to correct
  14. 14. Activity: risk register workshop
  15. 15. Formative rubric
  16. 16. Pair with this unit
  17. 17. Bridge

Slide 1 of 17

H01 electron microscopy with object segmentation and original 2 µm scale bar

Human cortex · H01
Object segmentation over electron microscopy

H01 release · Lichtman Lab / Harvard & Connectomics at Google · CC BY 4.0
Shapson-Coe et al. (2024) · doi:10.1126/science.adk4858

Technical Course · Unit 03

EM Prep and Imaging

Every preparation step solves a specific problem and introduces a characteristic failure. Learn the pairs.

Slide 2 of 17

Session outcomes (60 minutes)

Slide 3 of 17

Pedagogical arc

Slide 4 of 17

Acquisition chain (operational)

Fixation -> Staining -> Sectioning/block-face -> Imaging -> Stack assembly

Each stage introduces distinct, diagnosable error signatures.

Figure description and teaching notes

Walk the chain with its failure pairs from unit §1.
Fixation: slow or delayed fixation shows as swollen astrocytic processes and enlarged extracellular space — it can make segmentation easier and still distorts every geometric measurement.
Staining (rOTO: reduced osmium, thiocarbohydrazide, osmium, then uranyl acetate and lead aspartate): weak membrane contrast is the dominant cause of automated merge errors — the most expensive prep failure.
Embedding: dehydration shrinks tissue roughly 5–20% linearly; every absolute length is affected, so prefer within-volume ratios.
Sectioning: lost sections, folds, knife chatter, compression. Block-face: charging; FIB-SEM curtaining.
Imaging: SNR improves with the square root of dose — doubling SNR costs about 4x the acquisition time.

Slide 5 of 17

Before EM: what a light microscope could draw

A hand-drawn neuroanatomy plate from the light-microscopy era: whole-cell morphology, no synapses.

Source: assets_outreach source decks, Module12 L3 S02. Historical/context visual.

Figure description and teaching notes

Instructor script: "Everything on this plate is visible with a light microscope and sparse staining. Nothing on it tells you who connects to whom. The prep chain we are about to walk through exists to get from this to membranes 20 nm apart."
Then ask where, in the chain on the previous slide, errors become irreversible. Target answer: fixation and staining — contrast that is not in the tissue cannot be recovered by re-imaging.

Slide 6 of 17

Automated against manual, on the same tissue

The only way to learn your prep's merge rate is to push a pilot volume all the way through segmentation and proofreading.

Source: assets_outreach source decks, Module13 L2 S09. Historical/context visual.

Figure description and teaching notes

Instructor script: "Panel a is automated segmentation, panel b is manual annotation of the same field. The difference between them is the proofreading bill your prep protocol will generate."
Tie to the non-negotiable rule in unit §3: run a pilot reconstruction on something like 100 x 100 x 100 µm before full acquisition — align, segment, skeletonize, and have a human proofread a handful of neurons. It costs perhaps 1–2% of the project and is the only way to discover a bad staining protocol while you can still change it.

Slide 7 of 17

Real data: one section of human cortex

  • H01: a ~4 mm wedge of human temporal cortex.
  • 5,019 sections at 33.9 nm mean thickness.
  • 1.8 PB raw acquisition; 326 days of 61-beam microscope time.

Source: H01 release (Lichtman Lab, Harvard; Connectomics at Google), CC BY 4.0 · Shapson-Coe et al. 2024, doi:10.1126/science.adk4858 · site render.

Figure description and teaching notes

Instructor script: "This is one of 5,019 sections. Imaging all of them took 326 days on a 61-beam microscope and produced 1.8 petabytes." Note the H01 case study's warning: 1.8 PB is the raw acquisition and 1.4 PB the aligned volume — the two get conflated constantly, so say which you mean.
Work the unit's acquisition-time arithmetic alongside: 800 µm cube at 4 x 4 x 40 nm is 8 x 10^14 px; at 0.2 gigapixels per second that is about 46 days of continuous imaging, about 77 at 60% uptime — before sectioning, QA or re-imaging.

Slide 8 of 17

Real data: the resolution you must reach everywhere

  • Same sample at native 4 nm: membranes, mitochondria, vesicle clouds.
  • Synapse-finding needs this resolution across the whole volume.
  • The field of view here is ≈3.6 µm across.

Source: H01 release (Lichtman Lab, Harvard; Connectomics at Google), CC BY 4.0 · Shapson-Coe et al. 2024, doi:10.1126/science.adk4858 · site render.

Figure description and teaching notes

Instructor script: "Every membrane you see here was made visible by osmium. If the staining had been ten percent weaker, which of these boundaries would a network still find?"
This is the slide to land the asymmetry: noise that raises the split rate is recoverable; faint membranes that raise the merge rate are much less so. When trading dose against speed, protect membrane contrast.

Slide 9 of 17

Artifact taxonomy with downstream impact

Figure description and teaching notes

The cost distinction from unit §2 matters more than the taxonomy: a labor artifact means the reconstruction will be correct eventually, after proofreading hours; a data-loss artifact (lost section, fold, tear, severe beam damage) makes some question permanently unanswerable in that region. Report them separately — one quality score conceals exactly the distinction the project needs.
Diagnostic habit: ask which coordinate system the defect lives in. Follows block geometry -> staining penetration. Follows acquisition order -> beam or detector drift. Follows anatomy -> possibly real tissue composition.

Slide 10 of 17

QA gates before full ingest

Figure description and teaching notes

Example gates from the unit, stated in advance with numbers: stop if more than 2 consecutive sections are lost; review handling if cumulative loss exceeds 1%; stop if membrane contrast-to-noise drops more than 20% from baseline; flag sections with more than 5% fold area; re-run alignment if the 99th-percentile residual exceeds one voxel at native xy.
Instructor script: "The numbers are yours to set. What is not optional is setting them before you start, because a threshold chosen after seeing the data is not a threshold."

Slide 11 of 17

Pilot-first strategy

Slide 12 of 17

Throughput vs fidelity tradeoff

Figure description and teaching notes

Use the unit's triage check-yourself on a 20,000-section volume: rank (a) 4 scattered lost sections, (b) 4 consecutive lost sections, (c) charging on 15% of sections, (d) 10% weaker membrane contrast throughout. Answer, roughly b > d > c > a. Four consecutive losses is a 160 nm gap that cuts the volume in two; weaker contrast raises merges everywhere and cannot be fixed by re-imaging. The lesson: distribution matters more than count.

Slide 13 of 17

Misconceptions to correct

Figure description and teaching notes

On the third: per-tile timestamps are what let you ask whether a defect follows block position, anatomy or acquisition time. Without them you cannot ask the question at all, and they cannot be recovered later.

Slide 14 of 17

Activity: risk register workshop

For three artifact types, specify:

Slide 15 of 17

Formative rubric

Slide 16 of 17

Pair with this unit

Slide 17 of 17

Bridge

Next unit: infrastructure for robust reconstruction once acquisition is trusted.