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Jan 2021

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Subcellular RNA-seq for the Analysis of the Dendritic and Somatic Transcriptomes of Single Neurons
用于分析单个神经元树突和体细胞转录组的亚细胞 RNA-seq   

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Abstract

In neurons, local translation in dendritic and axonal compartments allows for the fast and on-demand modification of the local proteome. As the last few years have witnessed dramatic advancements in our appreciation of the brain’s neuronal diversity, it is increasingly relevant to understand how local translation is regulated according to cell type. To this end, both sequencing-based and imaging-based techniques have recently been reported. Here, we present a subcellular single cell RNA sequencing protocol that allows molecular quantification from the soma and dendrites of single neurons, and which can be scaled up for the characterization of several hundreds to thousands of neurons. Somata and dendrites of cultured neurons are dissected using laser capture microdissection, followed by cell lysis to release mRNA content. Reverse transcription is then conducted using an indexed primer that allows the downstream pooling of samples. The pooled cDNA library is prepared for and sequenced in an Illumina platform. Finally, the data generated are processed and converted into a gene vs. cells digital expression table. This protocol provides detailed instructions for both wet lab and bioinformatic steps, as well as insights into controls, data analysis, interpretations, and ways to achieve robust and reproducible results.


Graphic abstract:

Subcellular Single Cell RNA-seq in Neurons.


Keywords: Local translation (本地翻译), Local protein synthesis (局部蛋白质合成), Local transcriptome (本地转录组), Subcellular transcriptomics (亚细胞转录组学), Single cell RNA-seq (单细胞RNA-seq), Neuron (神经元), Dendrites (树突), Neuronal compartments (神经元隔室)

Background

Within their highly polarized and complex structure, neurons create subcellular compartments that optimize operations requiring spatial and temporal isolation. The functional specialization of these compartments is, in part, accomplished via the selective transport and local translation of mRNAs (Holt et al., 2019). To characterize the neuronal local transcriptome, many studies have performed bulk RNA profiles in brain regions enriched in dendrites and axons (Zhong et al., 2006; Cajigas et al., 2012; Glock et al., 2020), on synaptic particles isolated from tissue (Hafner et al., 2019), or from neuronal cultures in chambers that separate cell bodies and neurites (Gumy et al., 2011; Poon et al., 2006). These studies have revealed that protein functions such as synaptic transmission, cytoskeletal regulation, and translation itself (among others) are encoded in the local transcriptome (Holt et al., 2019). However, as recent advancements in single cell transcriptomics have revealed, the brain contains a complex array of neuronal types, raising the question of how variable the local transcriptome is across diverse cell types (Wang et al., 2020; Perez et al., 2021).


To address this question, single cell resolution of the local transcriptome is needed. There are three main challenges to implementing such an approach: (1) the isolation of mRNAs from distinct subcellular compartments of a single neuron, (2) the unbiased characterization of the local mRNAs, and (3) the characterization of hundreds to thousands of samples for robust cell type classification. Two pioneering studies used single-cell nanobiopsies or micropipettes to isolate material from the soma and dendrites of single neurons in culture, and RNA-seq to profile hundreds and thousands local mRNAs, respectively (Tóth et al., 2018; Middleton et al., 2019). However, both studies contained a relatively low number of samples (a few dozen), and neither investigated cell type effects on the local transcriptome. Recent breakthroughs in spatial transcriptomics have provided unprecedented in situ single molecule resolution of the local transcriptome, allowing for the characterization of mRNAs across and within subdomains of dendritic and axonal compartments. Using multiplexed error-robust fluorescence in situ hybridization, Wang et al. (2020) profiled the spatial location of hundreds of mRNAs in hundreds of cultured neurons, resulting in the identification of dendritic and axonal transcripts in glutamatergic and GABAergic neurons. However, this technique requires the experimenter to select a priori which mRNAs to target, and therefore, cannot provide an unbiased view of the local transcriptome. To circumvent this limitation, Alon et al. (2021) recently developed expansion sequencing (ExSeq), which combines expansion microscopy with fluorescent in situ sequencing. This technique allows the unbiased characterization of mRNAs anywhere within a neuron, in either cultured cells or tissue samples. However, so far untargeted ExSeq resolves only dozens of mRNA species per cell, and thus has not been used to profile cell type-specific variation in the local transcriptome.


Recently, we developed a method that allows for the separate isolation of dendritic and somatic mRNAs, as well as the unbiased characterization and molecular tallying of local mRNAs, in hundreds to thousands of single neurons (Perez et al., 2021). Using this method, we profiled the dendritic transcriptome of glutamatergic and various GABAergic interneurons in culture, and identified dozens of mRNAs whose dendritic localization is regulated according to cell-type. Cell-type specific differences are substantially more common among somata as expected, since besides its own transcriptome, the soma also harbors the transcriptome of dendritic and axonal compartments. Similar observations were made by Wang et al. (2020) using an alternative approach. Our method combines laser capture microdissection (LCM) for the isolation of dendritic and somatic compartments (micron resolution; Figure 1A), with a sensitive scRNA-seq protocol (adapted from Picelli et al., 2014; Macosko et al., 2015), which tags mRNAs with a unique molecular identifier (UMI) and an index (Figure 1B). This index allows iterative pooling steps during library preparation, enabling the sequencing of 384 samples per run. The protocol can be executed from beginning-to-end in two weeks, and, if repeated 3 times or more, thousands of subcellular samples can be accumulated. Since every cell is imaged before collection, this method can also be used to investigate correlations between the transcriptome and neuronal morphology. Nonetheless, several drawbacks of the protocol should be considered before starting. First, the method (as calculated using ERCC RNA standards) captures one of every 4 molecules present after LCM collection. This, however, likely overestimates the sensitivity to the actual number of molecules present inside the cell, as not all LCM catapulted pieces of cellular material land in the collection cap, an issue that appears more severe for dendrites. Thus, the more abundant an mRNA is, the more likely it is to be detected, and lower abundance mRNAs are more frequently missed. To compensate for this, we recommend increasing the number of samples until the number of detected mRNAs in dendrites reaches saturation. Second, the protocol requires the selection of neurons with little-to-no overlapping cellular processes in most of its dendritic arbor. This selection can introduce biases for some cell types over others. Indeed, we observed that, on average, GABAergic neurons have more accessible somata and processes, while glutamatergic processes are often heavily entangled with those of other cells. To estimate this potential bias, we suggest collecting somata-only samples from less accessible neurons in the same dish. The inclusion of such samples also improves unsupervised clustering, and thus enables more accurate cell-type determination of those neurons for which both soma and dendrites are collected.



Figure 1. Subcellular scRNA-seq method.

A. Images showing the dissection of the soma and dendrites of a neuron using LCM. B. Library preparation workflow, showing the sequences, primers, and key enzymes used at every step.


Altogether, this method can serve as a powerful tool to achieve an unbiased investigation of cell type effects in the local transcriptome, and can be implemented across cells derived from different brain regions, developmental stages, or species. Additionally, it may be used to study single cell responses to pharmacological treatments, or other manipulations that induce changes in cell states (e.g., paradigms of synaptic plasticity). It may also be useful to study the local transcriptome of other polarized cell types, such as astrocytes (Sakers et al., 2017; Mazaré et al., 2021), or epithelial cells (Moor et al., 2017). Finally, it should be possible to adjust this protocol to profile non-coding RNAs, such as small RNAs (Hagemann-Jensen et al., 2018).

Materials and Reagents

  1. Glass-bottom culture 35 mm dishes, 14 mm glass diameter (MatTek, catalog number: P35G-1.5-14-C)

  2. Qubit Assay Tubes (ThermoFisher, catalog number: Q32856)

  3. Reagent Reservoir 25 mL (VWR, catalog number: 89094-662)

  4. 96-well DNA LoBind Plate (Eppendorf, catalog number: 30129504)

  5. AdhesiveCap-200 Clear (Zeiss, catalog number: 415190-9191-000)

  6. Agencourt AMPure XP Magnetic Beads (Beckman Coulter, catalog number: A63881)

  7. Betaine (ThermoFisher, catalog number: J77507AE)

  8. Bioanalyzer HS-DNA Kit (Agilent, catalog number: 5067-4626)

  9. Cultured neurons. We use Rat hippocampus primary neuronal cultures (prepared as described in Aakalu et al., 2001).

  10. CustomSeqB Primer (IDT, see Supplementary File 1 with oligo information)

  11. Dithiothreitol (DTT) (Bio-Rad, catalog number: 1610611)

  12. dNTP mix (ThermoFisher, catalog number: R0192)

  13. Elution Buffer (Qiagen, catalog number: 1014609)

  14. ERCC RNA Spike-Ins (ThermoFisher, catalog number: 4456740)

  15. ISPCR Primer (IDT, see Supplementary File 1 with oligo information)

  16. KAPA HiFi HotStart Mix (Roche, catalog number: KK2602)

  17. Magnesium Chloride (ThermoFisher, catalog number: AM9530G)

  18. Microseal B adhesive film (Bio-Rad, catalog number: MSB1001)

  19. Molecular Biology Grade Ethanol (Sigma, catalog number: BP2818-100)

  20. Nextera XT DNA Library Prep Kit (96 samples) (Illumina, catalog number: FC-131-1096)

  21. Nextera XT Index Kit v2 Set A (Illumina, catalog number: FC-131-2001)

  22. Nextera XT Index Kit v2 Set B (Illumina, catalog number: FC-131-2002)

  23. NextSeq 1000/2000 P2 reagents (100 cycles) (Illumina, catalog number: 20046811)

  24. Nuclease-free H2O (ThermoFisher, catalog number: 10977-035)

  25. P5-ISPCR Primer (IDT, see Supplementary File 1 with oligo information)

  26. Parafilm (Sigma, catalog number: PM-992)

  27. Phosphate Buffer Saline (Sigma, catalog number: P5493-1L)

  28. Poly-D-lysine (Corning, catalog number: 354210)

  29. Qiagen Protease (Qiagen, catalog number: 19155)

  30. Qubit dsDNA BR kit (ThermoFisher, catalog number: Q32853)

  31. Qubit dsDNA HS kit (ThermoFisher, catalog number: Q32851)

  32. RNase Inhibitor (Takara, catalog number: 2313A)

  33. RNaseZap (ThermoFisher, catalog number: AM9780)

  34. RT-UMI-Index Primer (16 variants) (IDT, see Supplementary File 1 with oligo information)

  35. SingleShot Cell Lysis Kit (Bio-Rad, catalog number: 1725080)

  36. SuperScript IV (ThermoFisher, catalog number: 18090200)

  37. Template Switch Oligo (IDT, see Supplementary File 1 with oligo information)

  38. Cell Lysis Mix (see Recipes)

  39. RT Mix (see Recipes)

  40. PreAmp PCR Mix (see Recipes)

  41. Final PCR Amplification Mix (see Recipes)

Equipment

  1. 2-20, µL 12-channel, Multichannel pipette (e.g., Ranin, catalog number: 17013808)

  2. 20-200 µL, 12-channel, Multichannel pipette (e.g., Ranin, catalog number: 17013810)

  3. 2100 Bioanalyzer Instrument (Agilent, catalog number: G2939BA)

  4. Cell culture incubator (e.g., ThermoFisher)

  5. Curved, cover glass forceps (VWR, catalog number: HAMMHSC817-13)

  6. DISH 35 CC Adapter (Zeiss, catalog number: 415101-2000-835)

  7. Magnetic Stand for 96-well plates (ThermoFisher, catalog number: AM10027)

  8. Minicentrifuge (e.g., ThermoFisher, catalog number: 75004061)

  9. lllumina DNA Sequencer (e.g., NextSeq 2000)

  10. PALM MicroBeam Axio Observer Laser Capture Microdissection Microscope (Zeiss)

  11. PCR Plate Spinner (VWR, catalog number: 89184-610) or 96-well plate-compatible centrifuge

  12. Qubit Fluorometer (ThermoFisher, catalog number: Q33238)

  13. SingleCap Collector II 200 RM (Zeiss, catalog number: 415101-2000-951)

  14. Thermal cycler (e.g., Bio-Rad, C1000, catalog number: 1851197)

  15. Tissue culture hood (e.g., ThermoFisher)

  16. Vortex (e.g., VWR, catalog number: 10153-840)

Software

  1. PALM RoboSoftware v3 or above (Zeiss, https://www.zeiss.com/microscopy/us/products/microscope-software/palm-robosoftware.html/palm46)

  2. bcl2fastq v2.20.0.422 (Illumina, https://support.illumina.com/sequencing/sequencing_software/bcl2fastq-conversion-software.html)

  3. FastQC v.0.11.9 (Babraham Bioinformatics/FastQC, https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)

  4. Picard Tools v.2.20.2 or above (broadinstitute/picard, https://github.com/broadinstitute/picard/releases/tag/2.20.2)

  5. Drop-seq Tools v.2.3.0 or above (broadinstitute/Drop-seq, https://github.com/broadinstitute/Drop-seq/releases/tag/v2.3.0)

  6. STAR v.2.7.2b or above (alexdobin/STAR, https://github.com/alexdobin/STAR/releases/tag/2.7.2b)

  7. Fasta file containing the genome of the species being used. Genome fasta files of various species can be found in https://hgdownload.soe.ucsc.edu/downloads.html or http://ftp.ensembl.org/pub/release-99/fasta/

  8. GTF file containing transcript coordinates in the genome of the species being used. GTF files of various species can be found in https://hgdownload.soe.ucsc.edu/downloads.html or http://ftp.ensembl.org/pub/release-99/gtf/

  9. Seurat 3 or above (satijalab/seurat, https://satijalab.org/seurat/articles/install.html)

Procedure

  1. Determine the desired number of samples per sequencing run (see Note 1 for limitations and considerations). This procedure is designed for the sequencing analysis of 384 samples, including both positive and negative controls (see Note 2), which are indexed, and subsequently pooled in three separate steps (see Note 3).


  2. Before starting, clean pipettes, racks, centrifuges, and surfaces in the LCM microscope and bench space with RNaseZap, to avoid RNA degradation.


  3. Primary neuronal cultures

    1. Prepare mammalian neuronal cultures according to protocol of choice. In our case, we culture neurons from the hippocampus or cortex of newborn rats (P1), as previously described (Aakalu et al., 2001).

    2. Plate cells on coverslips of MatTek 14mm diameter glass bottom dishes coated with a 0.1 mg/mL solution of poly-D-lysine, at a density of 20,000 cells/coverslip.

    3. Maintain plated cells in the cell medium of choice for at least 2 weeks and no longer than 5 weeks in culture for best confluency of neuronal processes. Unless cell age is a variable of interest, we strongly recommend that all neurons in an experiment share the same age, as the transcriptome may vary significantly over the lifespan of cultured neurons.


  4. Ethanol fixation

    1. Discard cell medium.

    2. Wash cultures by adding 5 mL of 1× PBS and immediately discard it. Repeat once, for a total of 2 washes.

    3. Add 5 mL of cold 70% ethanol (kept at -20°C). Wait 5 min and discard ethanol.

    4. Seal the dish with parafilm and store at -80°C. RNA in cells will remain stable for at least 3 months.


  5. Laser capture microdissection

    1. Make 4 separate cell lysis master mixes, differing in which of the 16 RT-UMI-Index (1-16) Primer is used (see Recipe 1). Keep on ice.

    2. Remove parafilm and cap from dish and place it on the DISH 35 CC adapter of a Zeiss PALM LCM microscope. Cells should be allowed to thaw for 5-10 min before dissection. Proceed to the next step in the meantime.

    3. Open the PALMRobo software. Using the 20× objective, identify and register locations for microdissection. We recommend the collection of no more than 48 samples per plate (see Note 1): the somata and dendritic arbors of 16 neurons amenable for dissection, 12 somata whose dendritic arbors are not amenable for collection, and 4 empty cuts (see Note 2). Thus, at this point the location of 28 neurons and 4 empty regions should be saved. Neurons amenable for collection have isolated somata free of processes, AND isolated dendritic arbors in which most processes can be unambiguously assigned to the same neuron (See example in Figure 1A and Supplementary File 2). Avoid neurons at the edges of the coverslip, as these are inefficiently catapulted.

    4. Switch to the 40× objective, go to the first registered location, and take a picture.

    5. For somata or empty regions, select AutoLPC from the Cut Tools menu, and the circle drawing tool to delineate the soma or region of interest. For dendrites, select LineAutoLPC from the Cut Tools menu, and the free-hand drawing tool to delineate processes. Because of the continuous nature of neuronal compartments, there is no obvious point where the soma ends and dendrites begin, and thus what we consider the border between the two compartments is ultimately arbitrary. In our experiments, we delineate the soma as the cellular area containing and surrounding the nucleus, which dilates out until drastic decrements in width suddenly occur. The processes that continue are considered dendrites. However, processes that are qualitatively thinner than the rest are excluded as these might be axons (see example in Figure 1A, Supplementary File 2, and Video 1).


      Video 1. Delineation and laser capture microdissection of soma and dendritic processes of five single neurons.


    6. Place cap of an AdhesiveCap-200 clear tube in the microscope’s RoboMover containing the SingleCap Collector II 200 RM. Cut the stretch of the tube linking the cap to the tube, and discard the tube.

    7. Click on Capture Device icon, go to the Adjust tab, and set working height to -12,500. Go to Operation, and double-click on top of the cap icon. This will bring the cap in the collection position above the culture dish. Adjust focus if necessary, to obtain a clear image of the neuron.

    8. Set the following parameters for Laser Pressure Catapulting (LPC): Energy = 40, focus = 70%, and speed = 15%.

    9. Go to the Colors icon on the Graphic toolbar, and on the LPC Distances tab set the Distance of AutoLPC shots to 2. This determines the density of the LPC punches.

    10. Go to Element List, and select only the element delineating the soma. Click on the Start Cutting Laser icon. The area delineating the soma will be catapulted to the collection cap in many individual punches.

    11. Click on Capture Device icon, go to the Operation tab, and click on the Home icon. This will bring the cap back to the loading position.

    12. Using curved forceps, carefully remove the cap from the collection arm and place it on a surface, with the side where the material was catapulted into facing up.

    13. Add 3 µL of the respective Cell lysis master mix to the cap containing the catapulted material.

    14. Using curved forceps, carefully place the cap in a well of a 96-well DNA LoBind plate, with the side where the material was catapulted into facing down.

    15. Repeat steps 5 to 7.

    16. Go to Element List and select all of the elements delineating the dendrites of that particular neuron. Click on the Start Cutting Laser icon. The area delineating each dendrite will be catapulted to the collection cap in many individual punches.

    17. Repeat steps 11 to 14.

    18. Repeat steps 5 to 17 for each location registered in step 3. We recommend organizing samples shown in Figure 4A in each 96-well plate.

    19. Proceed immediately to the next step.


  6. Cell lysis

    1. Seal the plate containing collection caps tightly with Microseal B adhesive film, turn it upside down, and vortex the side containing caps for 15 s.

    2. Centrifuge plate for 1 min in plate spinner, or centrifuge for 1min at 1,000 × g, to bring volumes from the cap to the bottom of the well. Discard caps and reseal plate with Microseal B adhesive film.

    3. Place plate in thermal cycler and run the following program (lid set to 105°C):

      Step 1 (Protein Digestion): 50°C for 10 min

      Step 2 (Protease Inactivation): 75°C for 10 min

      Step 3: 4°C hold

    4. Proceed immediately to the next step.


  7. Reverse Transcription

    1. Prepare RT master mix (see Recipe 2).

    2. Split RT master mix into 12 PCR tubes, each containing 15.3 µL.

    3. Using a multichannel pipette, pipette 3.4 µL of RT master mix out of the 12 PCR tubes prepared in the previous step, and add it to each reaction.

    4. Seal plate with Microseal B adhesive film.

    5. Mix, by quick vortex and 1 min centrifugation in plate spinner, or in centrifuge for 1 min at 1,000 × g.

    6. Place plate in thermal cycler and run the following program (lid set to 105°C):

      Step 1 (Reverse Transcription): 55°C for 10 min

      Step 2 (Enzyme Inactivation): 80°C for 10 min

      Step 3: 12°C hold


  8. PCR pre-amplification

    1. Prepare PCR PreAmp master mix (see Recipe 3).

    2. Split PCR PreAmp master mix into 12 PCR tubes each containing 34.2 µL.

    3. Using a multichannel pipette, pipette 7.6 µL of PCR PreAmp master mix out of the 12 PCR tubes prepared in the previous step, and add it to each reaction.

    4. Seal plate with Microseal B adhesive film.

    5. Mix by quick vortex and 1min centrifugation in plate spinner, or centrifuge for 1 min at 1,000 × g.

    6. Place plate in thermal cycler and run the following program (lid set to 105°C):

      Step 1 (Taq Activation): 98°C for 3 min

      Step 2 (Denaturation): 98°C for 20 s

      Step 3 (Annealing): 67°C for 15 s

      Step 4 (Extension): 72°C for 6 min

      (Repeat steps 2-4 for a total of 21 cycles*)

      Step 5 (Final Extension): 72°C for 5 min

      Step 6: 12°C hold

      *Cycle number may need to be optimized, as samples with high starting amounts of RNA will require less cycles, and those with low starting amounts will require more.

    7. It’s safe to stop and store PCR reactions at -20°C for at least 3 months.

    8. Proceed to the next step once all 384 samples have been accumulated (~8 culture dishes).


  9. Sample pooling and PCR purification

    1. Let AMPure XP DNA Magnetic Beads stand at room temperature at least 30 min (see Note 3 for best practices when performing DNA purification using magnetic beads).

    2. Pool 8 reactions derived from the same source (i.e., somata, dendrites, or empty cuts), but which were obtained from different RT index primers, into a single well in a DNA LoBind 96-well plate (pooling logic is described in detail in Note 4). This should result in a plate with 4 rows in which each well contains ~112 µL of pooled sample.

    3. Add 112 µL of AMPure Magnetic Beads to each well. Seal plate. Mix well by vortexing.

    4. Let plate stand at room temperature for 5 min.

    5. Place plate in magnetic stand. Let stand for 5 min.

    6. Prepare fresh 80% ethanol, considering that 48 pooled samples require ~22 mL of 80% ethanol.

    7. Place 80% ethanol solution in 25 mL reservoir.

    8. Discard supernatant from every well containing pooled samples, being careful not to disrupt the magnetic bead pellet.

    9. Add 200 µL of freshly-made 80% ethanol to each well. Wait 30 s and remove.

    10. Repeat previous step one more time, making sure to discard all ethanol at the end (may require additional pipetting out).

    11. Let plate stand in magnetic stand for 5 min, or until the bead pellet looks dry. If cracks begin to appear in the pellet, proceed immediately to next step.

    12. Remove plate from magnetic stand, and add 17.5 µL of Elution Buffer (EB) on top of pellet.

    13. Resuspend pellet by vortexing and, if necessary, by repeatedly pipetting elution volume on top of pellet.

    14. Wait 5 min.

    15. Place tube back on magnetic stand and wait 2 min.

    16. Transfer 15 µL of supernatant to wells in a new 96-well DNA LoBind plate.

    17. Combine supernatant from 2 pooled samples derived from the same source (i.e., somata, dendrites, or empty cuts) that contained no overlap in the 8 indexes pooled in step 2 (see Figure 4). Thus, this mix will contain 16 different indexes, each representing a different sample.


  10. Quality metrics of cDNA libraries

    1. Check concentration of the 24 pooled samples using Qubit dsDNA BR Kit according to manufacturer’s instructions. Concentrations between 10-150 ng/µL per pooled sample are expected.

    2. Check size distribution of the 24 pooled samples using the Agilent Bioanalyzer HS-DNA kit, according to manufacturer’s instructions. Libraries are expected to have few or no peaks below 500bp and a large peak between ~800 bp and ~5,000 bp, centered at ~2,000 bp (Figure 2A).



      Figure 2. Expected size distribution of cDNA libraries.

      A. Bioanalyzer electropherogram from a pooled of dendritic samples after section J, step 2. B. Bioanalyzer electropherogram from same sample after section N, step 2.


  11. Tagmentation

    1. In a new 96-well DNA LoBind plate, make 0.1 ng/µL dilutions for each pooled sample.

    2. Add 10 µL of Nextera Tagment DNA Buffer to each well.

    3. Add 5 µL of Nextera Amplicon Tagment Mix to each well.

    4. Seal plate and centrifuge in plate spinner for 1 min.

    5. Incubate samples for 5 min in a thermal cycler set to 55°C (heated lid 105°C).

    6. Stop Tagmentation by adding 5 µL of Nextera Neutralize Tagment Buffer to each well.

    7. Seal plate and centrifuge in plate spinner for 1 min.

    8. Incubate plate at room temperature for 5 min.


  12. Amplification of sequencing-compatible fragments

    1. Prepare Final PCR master mix (see Recipe 4).

    2. Add 24 µL of Final PCR Amplification mix to each reaction.

    3. To each reaction, add 1 of the 24 different Nextera i7 primers (N7XX).

    4. Seal plate and centrifuge in plate spinner for 1 min.

    5. Place plate in thermal cycler and run the following program (lid set to 105°C):

      Step 1 (Pre-PCR Incubation): 98°C for 3 min

      Step 2 (Denaturation): 95°C for 30 s

      Step 3 (Denaturation): 95°C for 10 s

      Step 4 (Annealing): 55°C for 30 s

      Step 5 (Extension): 72°C for 30s

      (Repeat steps 2-4 for a total of 12 cycles)

      Step 6 (Final Extension): 72°C for 5 min

      Step 6: 12°C hold


  13. PCR cleanup and purification

    1. Let AMPure XP DNA Magnetic Beads stand at room temperature at least 30 min.

    2. Add 30 µL of AMPure XP DNA Magnetic Beads to each reaction. Mix well by vortexing.

    3. Repeat steps 4-15 of section I to purify amplified DNA.

    4. It’s safe to stop and store PCR reactions at -20°C. Sequence samples within 2 weeks.


  14. Quality metrics

    1. Check concentration of each sample using Qubit dsDNA HS Kit according to manufacturer’s instructions. Concentrations between 1-10 ng/µL per sample are expected.

    2. Check size distribution of each sample using the Agilent Bioanalyzer HS-DNA kit, according to manufacturer’s instructions. Libraries are expected to show weak a large peak between ~300 bp and ~1,000 bp, centered at ~500 bp (Figure 2B).


  15. Generation of 2 nM multiplexed library

    1. Normalize the concentration of each library to 2 nM.

    2. Combine 2 µL of each normalized library for a final volume of 48 µL. Mix well.

    3. Confirm concentration of final multiplexed library using Qubit dsDNA HS Kit according to manufacturer’s instructions. Adjust concentration to 2 nM if necessary.


  16. Paired-end sequencing

    1. Perform paired-sequencing according to manufacturer’s protocol. Here, we describe the process for NextSeq 2000 using Nextseq 1000/2000 P2 reagents (100 cycles) v3.

    2. Combine 12 µL of multiplexed sample with 12 µL of NextSeq RSB with Tween buffer. Vortex briefly and centrifuge for 1 min.

    3. Combine 1.8 µLof Read1 CustomSeqB primer with 600 µL of HT1 buffer. Vortex and centrifuge.

    4. Add 20 µL of diluted library to the bottom of the library well of the sequencing cartridge.

    5. Load 550 µL of Read1 CustomSeqB primer dilution into well #1 of sequencing cartridge.

    6. Follow manufactures instructions to start sequencing run.

    7. Select custom 1 for Read 1.

    8. Setup the following sequencing parameters and run: Read 1: 26 bp, Read 2: 82 bp, Read 1 Index: 8 bp.


  17. Generation of files needed for Drop-seq core computational protocol (see Note 5).

    1. Generate sequence dictionary using the following command:


      java -jar /path/to/picard/picard.jar CreateSequenceDictionary \

      REFERENCE=my.fasta \

      OUTPUT= my.dict \

      SPECIES=species_name


    2. Generate refFlat annotation file using the following command:


      /path/to/Drop-seq_tools/ConvertToRefFlat \

      ANNOTATIONS_FILE=my.gtf \

      SEQUENCE_DICTIONARY=my.dict \

      OUTPUT=my.refFlat


    3. Generate reduced GTF file using the following command:


      /path/to/Drop-seq_tools/ReduceGtf \

      GTF=my.gtf \

      SEQUENCE_DICTIONARY=my.dict \

      OUTPUT=my.reduced.gtf


    4. Generate intervals files using the following command:


      /path/to/Drop-seq_tools/CreateIntervalsFiles \

      REDUCED_GTF=my.reduced.gtf \

      SEQUENCE_DICTIONARY=my.dict \

      PREFIX=my \

      OUTPUT=/path/to/output/files \

      MT_SEQUENCE=chrM


    5. Generate genome directory for alignment process using the following command:


      /path/to/STAR/STAR \

      --runMode genomeGenerate \

      --runThreadN 8 \

      --genomeDir path/to/output/files\

      --genomeFastaFiles path/to/FASTA/file \

      --sjdbGTFfile path/to/GTF/file \

      --sjdbOverhang 81


  18. Data processing pipeline for the generation of digital gene expression tables.

    1. Demultiplex i7 indexes using the following command:


       bcl2fastq -runfolder-dir /path/to/rawdata/folder/ \

      - output-dir /path/to/output/folder/ \

      --no-lane-splitting \

      --loading-threads 8 \

      -- writing-threads 8 \

      --minimum-trimmed-read-length 0 \

      --mask-short-adapter-reads 0 \

      --sample-sheet /path/to/sample/sheet/


    2. Evaluate quality of sequencing data for all files using the following command:


      /path/to/fastqc *.fastq.gz

      (go to https://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/, for information on how to interpret results of FastQC quality metrics)

    3. For each i7-index sample, convert Fastq file to Sam file while merging R1 and R2 files using the following command:


      java -jar /path/to/picard/picard.jar CreateSequenceDictionary \

      F1= SampleX_R1.fastq.gz \

      F2= SampleX_R2.fastq.gz \

      O= SampleX.bam

      SM=SampleX


    4. Extract the RT index sequence of each read using the following command:


      /path/to/Drop-seq_tools/TagBamWithReadSequenceExtended \

      INPUT=SampleX.bam \

      OUTPUT=Indexed_SampleX.bam \

      SUMMARY= Indexed_SampleX.summary \

      BASE_RANGE= 9-16 \

      BASE_QUALITY=10 \

      DISCARD_READ=False \

      TAG_NAME=XC \

      NUM_BASES_BELOW_QUALITY=1


    5. Extract the molecular barcode sequence of each read using the following command:


      /path/to/Drop-seq_tools/TagBamWithReadSequenceExtended \

      INPUT=Indexed_SampleX.bam \

      OUTPUT=UMIed_SampleX.bam \

      SUMMARY= UMIed_SampleX.summary \

      BASE_RANGE= 1-8 \

      BASE_QUALITY=10 \

      DISCARD_READ=True \

      TAG_NAME=XM \

      NUM_BASES_BELOW_QUALITY=1


    6. Remove reads with low quality RT index or molecular barcode sequences using the following command:


      /path/to/Drop-seq_tools/FilterBam \

      TAG_REJECT=XQ \

      INPUT=UMIed_SampleX.bam \

      OUTPUT= Filtered_SampleX.bam


    7. Trim reads containing part of the template switch oligo using the following command:


      /path/to/Drop-seq_tools/TrimStartingSequence \

      INPUT=Filtered_SampleX.bam \

      OUTPUT= Trimmed_SampleX.bam \

      OUTPUT_SUMMARY= Trimmed_SampleX.summary \

      SEQUENCE= AAGCAGTGGTATCAACGCAGAGTGAATGGG \

      MISMATCHES=0 \

      NUM_BASES=5


    8. Trim polyA tails within reads using the following command:


      /path/to/Drop-seq_tools/PolyATrimmer \

      INPUT=Trimmed_SampleX.bam \

      OUTPUT= PolyATrimmed_SampleX.bam \

      OUTPUT_SUMMARY= PolyATrimmed_SampleX.summary \

      SEQUENCE= AAGCAGTGGTATCAACGCAGAGTGAATGGG \

      MISMATCHES=0 \

      NUM_BASES=6 \

      USE_NEW_TRIMMER =true


    9. Convert bam files back to Fastq format using the following command:


      java jar /path/to/picard/picard.jar SamToFastq \

      INPUT=PolyATrimmed_SampleX.bam \

      FASTQ= PolyATrimmed_SampleX.fastq


    10. Align reads to genome using the following command:


      /path/to/STAR/STAR \

      --runMode alignReads \

      --runTreadN 8 \

      --genomeDir path/to/genome/folder/ \

      --readFilesIn PolyATrimmed_SampleX.fastq \

      --outSAMtype BAM \

      --SortedByCoordinate \

      --alignSoftClipAtReferenceEnds No \

      --outFilterScoreMinOverLread 0.66 \

      --outFilterMatchNminOverLread 0.66


    11. Calculate quality metrics for RNA-sequencing using the following command:


      java jar /path/to/picard/picard.jar CollectRNASeqMetrics \

      I=Aligned_SampleX.bam \

      O= SampleX.RNA_Metrics \

      REF_FLAT = my.refFlat \

      STRAND=FIRST_READ_TRANSCRIPTION_STRAND \

      CHART_OUTPUT=SampleX_Metagene.plot \

      RRNA_FRAGMENT_PERCENTAGE=0.8 \

      MINIMUM_LENGTH=500 \

      RIBOSOMAL_INTERVALS=/path/to/my.intervals/rRNA.intervals


      (we expect the overwhelming majority of bases to map to mRNAs, and the metagene plot should show a strong 3’ bias as seen in Figure 3.)



      Figure 3. scRNA-seq data 3’ bias.

      Metagene plot showing the expected distribution of reads (normalized coverage) across the length of an mRNA, where 0 represents the 5’ most region and 100 the 3’ most region.


    12. Merge aligned bam file with the Indexed and UMIed bam file using the following command:


      java jar /path/to/picard/picard.jar MergeBamAlignment \

      REFERENCE_SEQUENCE= /path/to/Genome/fasta \

      UNMAPPED_BAM=UMIed_SampleX.bam \

      ALIGNED_BAM= Aligned_SampleX.bam \

      OUTPUT= Merged_SampleX.bam \

      INCLUDE_SECONDARY_ALIGNMENTS=false \

      PAIRED_RUN =false


    13. Tag reads with gene name using the following command:


      /path/to/Drop-seq_tools/TagReadWithGeneFunction \

      I=Merged_SampleX.bam \

      O= GeneTagged_SampleX.bam \

      ANNOTATIONS_FILE= my.refFlat


    14. Generate digital gene expression table using the following command:


      /path/to/Drop-seq_tools/DigitalExpression \

      I=GeneTagged_SampleX.bam \

      O= SampleX.DGE.gz \

      STRAND_STRATEGY=SENSE \

      SUMMARY=SampleX.DGE.summary.txt \

      CELL_BC_FILE=RT_Indexes*


      *This is a text file listing line-by-line the 8nt sequences of all indexes used.

Data analysis

  1. A dataset generated using this method on rat primary hippocampal neurons can be found in the NCBI Gene Expression Omnibus under the accession code GSE157204. A digital expression table generated from the data and a metadata file describing the experimental design can be found in Supplementary File 1 and Supplementary File 2 of our publication (Perez et al., 2021). The R source code used in analyses of the data can be found in our GitHub repository DOI: 10.5281/zenodo.4384479.

  2. The ERCCs Spike-Ins included in the protocol can be used to quantify the accuracy of the experiment, by comparing the input number of individual ERCCs with their average number in the sequenced results. A Pearson correlation >0.8 is expected. Also, the sensitivity of the experiment (how many of the mRNAs present in the lysis reaction were detected) can be calculated by determining the detection probability of ERCCs with different input values. On average, we detected 1 out of every 4 molecules present.

  3. Before analyzing the digital gene expression table, additional quality evaluations and data cleaning are necessary. First, ERCCs should be used to evaluate the quality of library preparation and sequencing: outliers with little to no ERCCs sequenced should be discarded. Second, somatic or dendritic samples with a number of RNA molecules comparable to that of empty cuts, should also be discarded. Finally, as described below, unsupervised dimensionality methods can reveal cell types present in the dataset. In our experience, this may reveal samples enriched in markers for apoptotic or glial cells, both of which should be discarded.

  4. To classify samples into types, use unsupervised dimensionality reduction methods such as UMAP or tSNE, and nearest neighbor approaches such as k-clustering, all of which are available in the Seurat package (Stuart et al., 2019). As dendritic transcriptomes are usually shallower than somatic ones, we perform cell type classifications based only on the somatic samples, and later extrapolate this information to their corresponding dendrites.

  5. Differential expression analyses can be performed both between the somata or dendrites of different cell types, or between the soma and dendrites of single neurons. For these, we recommend test based on logistic regression (Ntranos et al., 2019), or a Poisson generalized linear model (Stuart et al., 2019). When comparing soma versus dendrites, we recommend a paired-differential expression analysis using cell of origin as a latent variable.

Notes

  1. During the experimental design phase, several factors should be considered when choosing an appropriate N. Power analyses should be perform considering the types of tests and analyses that will be eventually performed with the data, as the required N to achieve statistical power varies from test to test. For differential expression analyses between cell types or between subcellular compartments, we suggest tools optimized for single cell datasets such as powsimR (Vieth et al., 2017) or scPower (Schmid et al., 2020). The number of samples that can be profiled in a single sequencing run depends on the multiplexing capacity of the experimental setup. In this protocol, the RT primer carries 1 of 16 different indexes while the i7 Nextera PCR step adds 1 of 24 indexes to each of the previous ones, allowing a maximum of (16×24) 384 samples to be simultaneously profiled. For us, this number provided sufficient sequencing depth per sample. However, it is certainly possible to increase the number of indexes and therefore the multiplexing capacity per run. In principle, all samples could be derived from a single plate but this is not recommended. As LCM occurs at room temperature, and only one sample is collected at a time, RNA integrity will significantly decrease overtime. We observed a trend to less RNA molecules detected per sample (suggestive of lower RNA integrity) after 3 h of collection. Thus, we suggest to limit the number of samples collected per dish to an amount that can be safely collected in 3 h (in our case, 48). We recommend using dishes from multiple primary neurons preparations, since variability between preparations occurs, and thus dishes from the same preparation do not yield fully independent samples.

  2. In addition to paired somatic and dendritic samples derived from the same single neurons, we recommend including two types of controls. To have a sense of the potential bias introduced by the selection of cells amenable for laser capture, we suggest collecting somata-only samples from less accessible neurons in the same dish. Secondly, as a negative control, we include samples in which Laser Pressure Catapulting is applied to regions in the dish that are devoid of soma and/or dendrites. The size of these cuts should be comparable to the area occupied by somata and/or dendrites. These samples serve to control for potentially contaminating extracellular RNAs, and can help set an expression cutoff to include a sample.

  3. When using magnetic beads to purify DNA, it is important to thoroughly resuspend beads by vortexing, before mixing them with the samples. It is also important to accurately pipette the desired volume in section I-step 3, as inaccuracies in volume can increase or decrease the presence of smaller DNA fragments. If possible, use low-binding tips at this step. It is also important to monitor that the pellet does not become too dry and begins to show cracks in section I-step 11, as this will result in reduced DNA concentrations after resuspension.

  4. To go from 384 collected samples to sequencing a single multiplexed sample, we incrementally pool at key steps of the protocol, namely steps 2 and 17 of section I, and step 2 of section N. To avoid pooling samples with the same index combination, and to perform the process rapidly and efficiently, we suggest the organization of samples based on the 96-well plate format illustrated in Figure 4.



    Figure 4. Sample pooling workflow.

    A. Pooling performed in section I, step 2. Two 96-well plates containing 48 samples each (only rows A-D are shown, as rows E-H are unoccupied). Somatic samples are placed in columns 1, 3, 5, and 7, and their respective dendrites are placed in columns 2, 4, 6 and 8. Control somata from less accessible areas are collected in columns 9, 10, and 11. Column 12 contains empty cuts (negative controls). Each row of the plate contains 1 of 16 different RT-UMI-Index primers. In section I step 2, all samples within the same columns of two complementary plates (containing a non-overlapping set of indexes) are pooled together. B. Pooling performed in section I, step 17. Two complementary samples within the same column are pooled together to reduce sample number to 24. C. Pooling performed in section N, step 2. After adding Nextera indexes, all samples are pooled together to reduce sample number to 1.


  5. Commands in sections P and Q are to be used in a command-line-interface in the directory where the analysis is performed.

Recipes

  1. Cell Lysis Master Mix (Table 1)


    Table 1. Cell Lysis Master Mix

    Cell Lysis Master Mix [Units] [Stock] [Final] 1 Rx (µl) 12Rx Master Mix (µl)1
    Nuclease-free H2O - - - 1.602 21.1
    SingleShot Lysis Buffer X 6.25 1 0.48 6.3
    Qiagen Protease mAU/ml 900 45 0.15 2
    dNTPs mM 10 1 0.64 8.4
    RT-UMI-Index(1-16) Primer µM 100 1 0.064 0.8
    Diluted ERCCs2 X 100 1 0.064 0.8
    - -- - Total 3 39.4


  2. RT Master Mix (Table 2)


    Table 2. RT Master Mix

    RT Master Mix [Units] [Stock] [Final]2 1 Rx (µl) 48Rx Master Mix (µl)1
    Nuclease-free H2O - - - 0.15 8.5
    SSIV buffer X 5 1 1.28 72
    MgCl2 mM 1000 6 0.04 2.2
    DTT mM 300 5 0.11 6
    Betaine M 5 1 1.28 72
    Template Switch Oligo µM 100 1 0.06 3.6
    RNase Inhibitor U/µl 40 1 0.16 9
    SuperScript IV U/µl 200 10 0.32 18
    - - - Total 3.4 191.3


  3. PCR PreAmp Master Mix (Table 3)


    Table 3. PCR PreAmp Master Mix

    PCR PreAmp Master Mix [Units] [Stock] [Final]4 1 Rx (µl) 48Rx Master Mix (µl)1
    Nuclease-free H2O - - - 0.59 33
    KAPA HiFi HotStart Mix X 2 1 7 393.8
    ISPCR Primer µM 100 0.1 0.014 0.8
    - - - Totals 7.6 427.5


  4. Final PCR Master Mix (Table 4)


    Table 4. Final PCR Master Mix

    Final PCR Master Mix [Units] [Stock] [Final]5 1 Rx (µl) 24Rx Master Mix (µl)1
    Nuclease-free H2O - - - 8 250
    Nextera PCR Master Mix X 3.3333 1 15 468.75
    P5-ISPCR Primer µM 10 0.2 1 31.25
    - - - Totals 24 750


1. Master mix volume is calculated to account for pipetting errors.

2. To make 100× ERCC solution, dilute ERCC RNA Spike-In Mix 1, 1:200,000.

3. Final concentrations for RT master mix reagents are calculated based on a final volume of 6.4 µL (3 µL of cell lysis mix + 3.4 µL RT mix).

4. Final concentrations for PCR PreAmp master mix reagents are calculated based on a final volume of 14 µL (6.4 µL of RT reaction + 7.6 µL of PCR PreAmp mix).

5. Final concentrations for Final PCR master mix reagents are calculated based on a final volume of 50 µL (26 µL of Tagmentation reaction + 24 µL of Final PCR mix).

Acknowledgments

This work was supported by the Max Planck Society, and the Advanced Investigator award from the European Research Council (grant 743216), DFG CRC 1080: Molecular and Cellular Mechanisms of Neural Homeostasis, and DFG CRC 902: Molecular Principles of RNA-based Regulation. We thank Dr. Susanne tom Dieck, Ivy CW. Chan and current and past members of the Schuman lab for helpful discussions and advice. This protocol is derived from our previous work (Perez et al., 2021; DOI: 10.7554/eLife.63092).

Competing interests

The authors declare no conflict of interest.

Ethics

The procedures involving animal care are conducted in conformity with the institutional guidelines that are in compliance with the national and international laws and policies (DIRECTIVE2010/63/EU; German animal welfare law, FELASA guidelines) and approved by and reported to the local governmental supervising authorities (Regierungsprasidium Darmstadt). The animals were euthanized according to annex 2 of 2 Abs. 2 Tierschutz-Versuchstier-Verordnung.

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简介

[摘要]在神经元中,树突和轴突隔室的局部翻译允许对局部蛋白质组进行快速和按需修改。由于过去几年见证了我们对大脑神经元多样性的认识的巨大进步,因此了解如何根据细胞类型调节局部翻译变得越来越重要。为此,最近报道了基于测序和基于成像的技术。在这里,我们提出了一种亚细胞单细胞 RNA 测序协议,该协议允许对单个神经元的体细胞和树突进行分子量化,并且可以扩大规模以表征数百到数千个神经元。使用激光捕获显微切割法解剖培养的神经元的胞体和树突,然后进行细胞裂解以释放 mRNA 含量。然后使用允许下游汇集样本的索引引物进行逆转录。汇集的 cDNA 文库在 Illumina 平台上进行准备和测序。最后,对生成的数据进行处理并转换为基因与细胞的数字表达表。该协议提供了湿实验室和生物信息学步骤的详细说明,以及对控制、数据分析、解释和实现稳健和可重复结果的方法的见解。

图文摘要:


神经元中的亚细胞单细胞 RNA-seq。

[背景] 在其高度极化和复杂的结构中,神经元创建亚细胞隔室,优化需要空间和时间隔离的操作。这些隔室的功能特化部分是通过 mRNA 的选择性转运和局部翻译来实现的 (霍尔特等人,2019)。为了表征神经元局部转录组,许多研究已经对富含树突和轴突的大脑区域进行了大量的 RNA 谱分析(Zhong等人,2006;Cajigas等人,2012;Glock等人,2020),对从分离的突触颗粒组织(Hafner等人,2019 年),或来自分离细胞体和神经突的腔室中的神经元培养物(Gumy等人,2011 年;Poon等人,2006 年)。这些研究表明,突触传递、细胞骨架调节和翻译本身(以及其他)等蛋白质功能是在局部转录组中编码的(Holt等人,2019 年)。然而,正如单细胞转录组学的最新进展所揭示的那样,大脑包含一系列复杂的神经元类型,这引发了一个问题,即局部转录组在不同细胞类型中的可变性有多大(Wang等人,2020 年;Perez等人,2021 年) )。
为了解决这个问题,需要本地转录组的单细胞分辨率。实施这种方法存在三个主要挑战:(1) 从单个神经元的不同亚细胞区室中分离 mRNA,(2) 局部 mRNA 的无偏见表征,以及 (3) 数百到数千个样本的表征用于稳健的细胞类型分类。两项开创性研究使用单细胞纳米活检或微量移液器从培养物中单个神经元的胞体和树突中分离材料,并使用 RNA-seq 分别分析成百上千的局部 mRNA(Tóth等人,2018 年;Middleton等人, 2019)。然而,这两项研究都包含相对较少的样本(几十个),并且都没有研究细胞类型对局部转录组的影响。空间转录组学的最新突破为局部转录组提供了前所未有的原位单分子分辨率,允许在树突和轴突区室的亚域内和亚域内表征 mRNA。使用多重抗错荧光原位杂交,Wang等人。 (2020) 分析了数百个培养神经元中数百个 mRNA 的空间位置,从而鉴定了谷氨酸能和 GABA 能神经元中的树突和轴突转录物。然而,这种技术要求实验者先验地选择要靶向的 mRNA,因此,不能提供对局部转录组的公正看法。为了规避这一限制,Alon等人。 (2021) 最近开发了扩展测序 (ExSeq),它将扩展显微镜与荧光原位测序相结合。这种技术允许对神经元内任何地方的 mRNAs 进行无偏见的表征,无论是在培养的细胞还是组织样本中。然而,到目前为止,非靶向 ExSeq 只能解析每个细胞的数十种 mRNA 种类,因此尚未用于分析局部转录组中细胞类型特异性的变异。
最近,我们开发了一种方法,允许在数百到数千个单个神经元中单独分离树突和体细胞 mRNA,以及局部 mRNA 的无偏表征和分子计数(Perez等人,2021)。使用这种方法,我们分析了培养物中谷氨酸能和各种 GABA 能中间神经元的树突转录组,并鉴定了数十种树突定位受细胞类型调节的 mRNA。正如预期的那样,细胞类型特异性差异在胞体中更为常见,因为除了其自身的转录组外,胞体还包含树突和轴突区室的转录组。 Wang等人也进行了类似的观察。 (2020) 使用另一种方法。我们的方法将激光捕获显微切割 (LCM) 用于分离树突和体细胞室(微米分辨率;图 1A)与敏感的 scRNA-seq 协议(改编自 Picelli等人,2014;Macosko等人,2015),它用唯一的分子标识符 (UMI) 和索引标记 mRNAs (图 1B)。该索引允许在文库制备过程中进行迭代合并步骤,每次运行可对 384 个样本进行测序。该方案可以在两周内从头到尾执行,如果重复3次或更多次,可以积累数千个亚细胞样本。由于在收集之前对每个细胞进行成像,因此该方法也可用于研究转录组和神经元形态之间的相关性。尽管如此,在开始之前应考虑该协议的几个缺点。首先,该方法(使用 ERCC RNA 标准计算)捕获 LCM 收集后每 4 个分子中的一个。然而,这可能高估了对细胞内实际分子数量的敏感性,因为并非所有 LCM 弹射的细胞材料碎片都落在收集帽中,这对于树突来说似乎更为严重。因此,mRNA 越丰富,就越有可能被检测到,而较低丰度的 mRNA 更容易被遗漏。为了弥补这一点,我们建议增加样本数量,直到在树突中检测到的 mRNA 数量达到饱和。其次,该协议要求在其大部分树突状乔木中选择几乎没有重叠细胞过程的神经元。这种选择可能会导致某些细胞类型相对于其他细胞类型出现偏差。事实上,我们观察到,平均而言,GABA 能神经元具有更容易接近的胞体和过程,而谷氨酸能过程通常与其他细胞的过程严重纠缠在一起。为了估计这种潜在的偏差,我们建议在同一个培养皿中从不易接近的神经元中收集仅躯体样本。包含此类样本还改善了无监督聚类,从而能够更准确地确定收集体细胞和树突的那些神经元的细胞类型。


图 1. 亚细胞 scRNA-seq 方法。
A. 显示使用 LCM 解剖神经元的胞体和树突的图像。 B. 文库制备工作流程,显示每一步使用的序列、引物和关键酶。

总而言之,这种方法可以作为一个强大的工具来实现对局部转录组中细胞类型效应的公正调查,并且可以在来自不同大脑区域、发育阶段或物种的细胞中实施。此外,它还可用于研究单细胞对药物治疗的反应,或其他诱导细胞状态变化的操作(例如,突触可塑性的范例)。研究其他极化细胞类型的局部转录组也可能有用,例如星形胶质细胞 (Sakers et al ., 2017; Mazaré et al ., 2021) 或上皮细胞 (Moor et al ., 2017)。最后,应该可以调整该协议以分析非编码 RNA,例如小 RNA(Hagemann-Jensen等人,2018)。

关键字:本地翻译, 局部蛋白质合成, 本地转录组, 亚细胞转录组学, 单细胞RNA-seq, 神经元, 树突, 神经元隔室

材料和试剂


1. 玻璃底培养35毫米培养皿,14毫米玻璃直径(MatTek,目录号:P35G-1.5-14-C

2. Qubit Assay TubesThermoFisher,目录号:Q32856

3. 试剂容器25 ml VWR,目录号:89094-662

4. 96DNA LoBind板(Eppendorf,目录号:30129504

5. AdhesiveCap-200 ClearZeiss,目录号:415190-9191-000

6. Agencourt AMPure XP 磁珠(Beckman Coulter,目录号:A63881

7. 甜菜碱(ThermoFisher,目录号:J77507AE

8. Bioanalyzer HS-DNA 试剂盒(Agilent,目录号:5067-4626

9. 培养的神经元。我们使用大鼠海马原代神经元培养物(如Aakalu等人2001中所述制备)。

10. CustomSeqB 引物(IDT,参见补充文件 1和寡核苷酸信息)

11. 二硫苏糖醇(DTT)(Bio-Rad,目录号:1610611

12. dNTP 混合物(ThermoFisher,目录号:R0192

13. 洗脱缓冲液(Qiagen,目录号:1014609

14. ERCC RNA Spike-InsThermoFisher,目录号:4456740

15. ISPCR 引物(IDT,参见补充文件 1和寡核苷酸信息)

16. KAPA HiFi HotStart MixRoche,目录号:KK2602

17. 氯化镁(ThermoFisher,目录号:AM9530G

18. Microseal B粘合膜(Bio-Rad,目录号:MSB1001

19. 分子生物学级乙醇(Sigma,目录号:BP2818-100

20. Nextera XT DNA Library Prep Kit96 个样品)(Illumina,目录号:FC-131-1096

21. Nextera XT Index Kit v2 Set AIllumina,目录号:FC-131-2001

22. Nextera XT Index Kit v2 Set BIllumina,目录号:FC-131-2002

23. NextSeq 1000/2000 P2 试剂(100 个循环)(Illumina,目录号:20046811

24. 无核酸酶 H 2 OThermoFisher,目录号:10977-035

25. P5-ISPCR 引物(IDT,参见补充文件 1和寡核苷酸信息)

26. 封口膜(Sigma,目录号:PM-992

27. 磷酸盐缓冲液(Sigma,目录号:P5493-1L

28. -D-赖氨酸(Corning,目录号:354210

29. Qiagen蛋白酶(Qiagen,目录号:19155

30. Qubit dsDNA BR 试剂盒(ThermoFisher,目录号:Q32853

31. Qubit dsDNA HS 试剂盒(ThermoFisher,目录号:Q32851

32. RNase抑制剂(Takara,目录号:2313A

33. RNaseZapThermoFisher,目录号:AM9780

34. RT-UMI-Index Primer16 种变体)(IDT,参见补充文件 1和寡核苷酸信息)

35. SingleShot 细胞裂解试剂盒(Bio-Rad,目录号:1725080

36. SuperScript IVThermoFisher,目录号:18090200

37. 模板开关寡核苷酸(IDT,请参阅带有寡核苷酸信息的补充文件 1

38. 细胞裂解混合物(参见食谱)

39. RT 混合(见食谱)

40. PreAmp PCR Mix(见配方)

41. 最终 PCR 扩增混合物(见配方)

 

设备

 

1. 2-20,微升 12 通道,多通道移液器(例如Ranin,目录号:17013808

2. 20-200 µl12 通道,多通道移液器(例如Ranin,目录号:17013810

3. 2100 Bioanalyzer InstrumentAgilent,目录号:G2939BA

4. 细胞培养箱(例如ThermoFisher

5. 弯曲的盖玻片钳(VWR,目录号:HAMMHSC817-13

6. DISH 35 CC 适配器( Zeiss,目录号:415101-2000-835

7. 用于 96 孔板的磁性支架( ThermoFisher,目录号:AM10027

8. 微型离心机(例如ThermoFisher,目录号:75004061

9. lllumina DNA 测序仪(例如NextSeq 2000

10. PALM MicroBeam Axio Observer 激光捕获显微切割显微镜(蔡司

11. PCR Plate SpinnerVWR目录号: 89184-610)或 96 孔板兼容离心机

12. Qubit 荧光计( ThermoFisher,目录号:Q33238

13. SingleCap Collector II 200 RM蔡司,目录号:415101-2000-951

14. 热循环仪(例如Bio-RadC1000,目录号:1851197

15. 组织培养罩(例如ThermoFisher

16. 涡流(例如VWR,目录号:10153-840

 

软件


1. PALM RoboSoftware v3 或更高版本(蔡司, https: //www.zeiss.com/microscopy/us/products/microscope-software/palm-robosoftware.html#palm46

2. bcl2fastq v2.20.0.422Illuminahttps://support.illumina.com/sequencing/sequencing_software/bcl2fastq-conversion-software.html

3. FastQC v.0.11.9Babraham Bioinformatics/FastQChttps: //www.bioinformatics.babraham.ac.uk/projects/fastqc/

4. Picard Tools v.2.20.2 或更高版本 (broadinstitute/picard, https://github.com/broadinstitute/picard/releases/tag/2.20.2 )

5. Drop-seq 工具 v.2.3.0 或更高版本 (broadinstitute/Drop-seq, https://github.com/broadinstitute/Drop-seq/releases/tag/v2.3.0 )

6. STAR v.2.7.2b 或更高版本 (alexdobin/STAR, https://github.com/alexdobin/STAR/releases/tag/2.7.2b )

7. Fasta 文件包含正在使用的物种的基因组。各种物种的基因组 fasta 文件可以在https://hgdownload.soe.ucsc.edu/downloads.htmlhttp://ftp.ensembl.org/pub/release-99/fasta/中找到

8. GTF 文件包含正在使用的物种基因组中的转录坐标。各种物种的 GTF 文件可以在https://hgdownload.soe.ucsc.edu/downloads.htmlhttp://ftp.ensembl.org/pub/release-99/gtf/找到

9. Seurat 3 或以上 (satijalab/seurat, https://satijalab.org/seurat/articles/install.html )


程序


A. 确定每次测序运行所需的样本数量(有关限制和注意事项,请参见注释 1)。该程序设计用于对 384 个样本进行测序分析,包括阳性和阴性对照(见注 2),这些样本被编入索引,随后在三个单独的步骤中合并(见注 3)。

 

B. 在开始之前,用 RNaseZap 清洁 LCM 显微镜和工作台空间中的移液器、架子、离心机和表面,以避免 RNA 降解。

 

C. 原代神经元培养物

1. 根据选择的协议准备哺乳动物神经元培养物。在我们的案例中,我们从新生大鼠 (P1) 的海马或皮层培养神经元,如前所述 (Aakalu et al ., 2001)

2. MatTek 14mm 直径玻璃底皿盖玻片上的板细胞涂有 0.1 mg/mL -D-赖氨酸溶液,密度为 20,000 个细胞/盖玻片。

3. 将镀层细胞在所选细胞培养基中保持至少 2 周且不超过 5 周,以实现神经元过程的最佳融合。除非细胞年龄是一个感兴趣的变量,否则我们强烈建议实验中的所有神经元共享相同的年龄,因为转录组可能在培养的神经元的生命周期内显着变化。

 

D. 乙醇固定

1. 丢弃细胞培养基。

2. × PBS清洗培养物并立即将其丢弃。重复一次,共洗 2 次。

3. 加入 5 ml 冷的 70% 乙醇(保持在 -20°C)。等待 5 分钟并丢弃乙醇。

4. 用封口膜密封盘子并储存在-80°C。细胞中的 RNA 将保持稳定至少 3 个月。

 

E. 激光捕获显微切割

1. 制作 4 种单独的细胞裂解预混液,使用 16 RT-UMI-Index (1-16) 引物中的哪一种(参见配方 1)。继续冰上。

2. 从盘子上取下封口膜和盖子,并将其放在蔡司 PALM LCM 显微镜的 DISH 35 CC 适配器上。解剖前应允许细胞解冻 5-10 分钟。同时进行下一步。

3. 打开 PALMRobo 软件。使用 20 ×物镜,识别和注册显微解剖位置。我们建议每盘收集不超过 48 个样本(见注 1):16 个适合解剖的神经元的胞体和树突乔木,12 个不适合收集的树突乔木的胞体,以及 4 个空切(见注 2.因此,此时应保存 28 个神经元和 4 个空白区域的位置。适合收集的神经元具有无进程的孤立体细胞和孤立的树突乔木,其中大多数进程可以明确分配给同一神经元(参见图 1A 补充文件 2中的示例)。避免盖玻片边缘的神经元,因为这些神经元的弹射效率低下。

4. 切换到40 ×物镜,到第一个注册的位置,拍照。

5. 对于 soma 或空白区域,从 Cut Tools 菜单中选择 AutoLPC,然后选择圆形绘图工具来描绘 soma 或感兴趣的区域。对于树突,从切割工具菜单中选择 LineAutoLPC,然后使用手绘工具来描绘过程。由于神经元隔室的连续性,没有明显的体细胞结束和树突开始的点,因此我们认为两个隔室之间的边界最终是任意的。在我们的实验中,我们将胞体描述为包含和围绕细胞核的细胞区域,细胞核向外扩张,直到宽度突然急剧减小。继续进行的过程被认为是树突。然而,质量上比其他过程更薄的过程被排除在外,因为这些过程可能是轴突(参见图 1A补充文件 2和视频 1中的示例)。

 

 

视频 1.五个单个神经元的体细胞和树突过程的描绘和激光捕获显微解剖。

 

6.  AdhesiveCap-200 透明管的盖子放入装有 SingleCap Collector II 200 RM 的显微镜 RoboMover 中。切断将盖子连接到管子上的管子,并丢弃管子。

7. 单击捕获设备图标,转到调整选项卡,并将工作高度设置为 -12,500。转到操作,然后双击帽子图标的顶部。这会将盖子放在培养皿上方的收集位置。必要时调整焦点,以获得神经元的清晰图像。

8. 为激光压力弹射 (LPC) 设置以下参数:能量 = 40,焦点 = 70%,速度 = 15%

9. 转到图形工具栏上的颜色图标,然后在 LPC 距离选项卡上将 AutoLPC 镜头的距离设置为 2。这决定了 LPC 打孔的密度。

10. 转到元素列表,并仅选择描绘体细胞的元素。单击开始切割激光图标。划定胞体的区域将在许多单独的拳头中弹射到收集帽上。

11. 单击捕获设备图标,转到操作选项卡,然后单击主页图标。这将使盖子回到装载位置。

12. 使用弯曲的镊子,小心地从收集臂上取下盖子并将其放在一个表面上,使材料弹射到的一侧朝上。

13.  3 µl 相应的细胞裂解预混液添加到含有弹射材料的盖子中。

14. 使用弯曲的镊子,小心地将盖子放入 96 DNA LoBind 板的孔中,使材料弹射到的一侧朝下。

15. 重复步骤 5 7

16. 转到元素列表并选择所有描绘该特定神经元树突的元素。单击开始切割激光图标。描绘每个枝晶的区域将在许多单独的冲头中弹射到收集帽上。

17. 重复步骤 11 14

18. 对步骤 3 中注册的每个位置重复步骤 5 17。我们建议在每个 96 孔板中组织图 4A 中所示的样本。

19. 立即进行下一步。

 

F. 细胞裂解

1. Microseal B 胶膜将装有收集盖的板紧紧密封,将其倒置,然后将装有盖的一侧涡旋 15 秒。

2. 在板式旋转器中将板离心 1 分钟,或 1,000 ×离心 1 分钟 g ,将体积从盖子带到井底。用Microseal B 胶膜丢弃瓶盖和重新密封板。

3. 将板放入热循环仪并运行以下程序(盖子设置为 105°C):

1 步(蛋白质消化):50°C 10 分钟

2 步(蛋白酶灭活):75°C 10 分钟

3 步:4°C 保持

4. 立即进行下一步。

 

G. 逆转录

1. 准备 RT 预混液(参见配方 2

2.  RT 主混合物分成 12 PCR 管,每个管含有 15.3 μL

3. 使用多通道移液器,从上一步准备的 12 PCR 管中移取 3.4 μL RT 主混合物,并将其添加到每个反应中。

4. Microseal B 胶膜密封板。

5. 通过快速涡旋混合,在板式旋转器中离心 1 分钟,或在 1,000 ×离心机中离心 1 分钟 

6. 将板放入热循环仪并运行以下程序(盖子设置为 105°C):

1 步(逆转录):55°C 10 分钟

2 步(酶灭活):80°C 10 分钟

3 步:12°C 保持

 

H. PCR预扩增

1. 准备 PCR 前置放大器预混液(参见配方 3)。

2.  PCR PreAmp 主混合物分成 12 PCR 管,每个管含有 34.2 µL

3. 使用多通道移液器,从上一步准备的 12 PCR 管中移取 7.6 μL PCR PreAmp 主混合物,并将其添加到每个反应中。

4. Microseal B 胶膜密封板。

5. 通过快速涡旋混合并在板式旋转器中离心 1 分钟,或以 1,000 ×离心 1 分钟 

6. 将板放入热循环仪并运行以下程序(盖子设置为 105°C):

1 步(Taq 激活):98°C 3 分钟

2 步(变性):98 °C 20

3 步(退火):67°C 15

4 步(延伸):72°C 6 分钟

(重复步骤 2-4 21 个循环*

5 步(最终延伸):72°C 5 分钟

6 步:12°C 保持

*可能需要优化循环数,因为起始 RNA 量高的样品需要的循环次数更少,而起始量低的样品需要的循环次数更多。

7.  -20°C 下停止和储存 PCR 反应至少 3 个月是安全的。

8. 一旦积累了所有 384 个样本(约 8 个培养皿),请继续执行下一步。

 

I. 样品汇集和 PCR 纯化

1.  AMPure XP DNA 磁珠在室温下放置至少 30 分钟(使用磁珠进行 DNA 纯化时,请参见注释 3 以了解最佳做法)。

2. 将来自相同来源(,胞体、树突或空切口)但从不同 RT 指数引物获得的池 8 反应放入 DNA LoBind 96 孔板中的单个孔中(池逻辑详细描述在注 4)。这应该会产生一个有 4 行的板,其中每个孔包含 ~112 μL的汇集样本。

3. 加入 112 μL AMPure 磁珠。密封板。通过涡旋充分混合。

4. 让板在室温下静置 5 分钟。

5. 将盘子放在磁性支架上。静置 5 分钟。

6. 准备新鲜的 80% 乙醇,考虑到 48 个混合样品需要约 22 mL 80% 乙醇。

7.  80% 乙醇溶液放入 25 mL水库中。

8. 从每个含有混合样品的孔中丢弃上清液,注意不要破坏磁珠颗粒。

9. 加入 200 μL新鲜制作的 80% 乙醇。等待 30 秒并移除。

10. 再次重复上一步,确保最后丢弃所有乙醇(可能需要额外的移液)。

11. 让板在磁力架中放置 5 分钟,或直到珠粒看起来干燥。如果颗粒开始出现裂缝,请立即进行下一步。

12. 从磁性支架上取下板,并在颗粒顶部添加 17.5 μL的洗脱缓冲液 (EB)

13. 通过涡旋重悬颗粒,如有必要,在颗粒顶部反复吹打洗脱体积。

14. 等待 5 分钟。

15. 将管放回磁性支架上并等待 2 分钟。

16.  15 μL的上清液转移到新的 96 DNA LoBind 板中的孔中。

17. 合并来自同一来源(,胞体、树突或空切口)的 2 个汇集样本的上清液,这些样本在步骤 2 中汇集的 8 个索引中不包含重叠(参见图 4)。因此,这个组合将包含 16 个不同的索引,每个索引代表一个不同的样本。

 

J. cDNA文库的质量指标

1. 根据制造商的说明,使用 Qubit dsDNA BR Kit 检查 24 个混合样本的浓度。预计每个混合样品的浓度在 10-150 ng/ µL之间。

2. 根据制造商的说明,使用 Agilent Bioanalyzer HS-DNA 试剂盒检查 24 个混合样品的大小分布。预计图书馆在 500 bp 以下的峰很少或没有峰,在 ~ 800 bp ~ 5,000 bp 之间有一个大峰,以 ~ 2,000 bp 为中心(图 2A)。

 


2. cDNA 文库的预期大小分布。

A. J 部分第 2 步之后来自树突样品池的生物分析仪电泳图。 B. N 部分第 2 步之后来自同一样品的生物分析仪电泳图。

 

K. 标记

1. 在新的 96 DNA LoBind 板中,为每个混合样本进行 0.1 ng/ µL稀释。

2. 加入 10 μL Nextera Tagment DNA 缓冲液。

3. 加入 5 μL Nextera Amplicon Tagment Mix

4. 密封板并在板旋转器中离心 1 分钟。

5. 在设置为 55°C(加热盖 105°C)的热循环仪中孵育样品 5 分钟。

6. 添加 5 µL Nextera 中和标记缓冲液来停止标记。

7. 密封板并在板旋转器中离心 1 分钟。

8. 在室温下孵育板 5 分钟。

 

L. 测序兼容片段的扩增

1. 准备最终 PCR 主混合物(参见配方 4)。

2. 在每个反应中加入 24 μL的最终 PCR 扩增混合物。

3. 在每个反应中,添加 24 种不同 Nextera i7 引物 (N7XX) 中的一种。

4. 密封板并在板旋转器中离心 1 分钟。

5. 将板放入热循环仪并运行以下程序(盖子设置为 105°C):

步骤 1PCR 前孵育):98°C 3 分钟

2 步(变性):95°C 30

3 步(变性):95°C 10

4 步(退火):55°C 30

5 步(延伸):72°C 30

(重复步骤 2-4 12 个循环)

6 步(最终延伸):72°C 5 分钟

6 步:12°C 保持

 

M. PCR 净化和纯化

1.  AMPure XP DNA 磁珠在室温下放置至少 30 分钟。

2. 在每个反应中加入 30 μL AMPure XP DNA 磁珠。通过涡旋充分混合。

3. 重复第 I 部分的步骤 4-15 以纯化扩增的 DNA

4.  -20°C 下停止和储存 PCR 反应是安全的。在 2 周内对样品进行测序。

 

N. 质量指标

1. 根据制造商的说明,使用 Qubit dsDNA HS Kit 检查每个样品的浓度。预计每个样品的浓度在 1-10 ng/ µL之间。

2. 根据制造商的说明,使用 Agilent Bioanalyzer HS-DNA 试剂盒检查每个样品的大小分布。预计图书馆将在约 300 bp 和约 1,000 bp 之间显示出弱的大峰,以约 500 bp 为中心(图 2B)。

 

O. 生成 2 nM 多路复用库

1. 将每个库的浓度标准化为 2 nM

2. 结合每个标准化库的2 μL ,最终体积为 48 μL 。搅拌均匀。

3. 根据制造商的说明,使用 Qubit dsDNA HS Kit 确认最终多路复用库的浓度。如有必要,将浓度调整为 2 nM

 

P. 双端测序

1. 根据制造商的协议执行配对测序。在这里,我们描述了 NextSeq 2000 使用 Nextseq 1000/2000 P2 试剂(100 个循环)v3 的过程。

2.  12 μL 的多路复用样品与 12 μL NextSeq RSB Tween 缓冲液结合。短暂涡旋并离心 1 分钟。

3.  1.8 μLof Read1 CustomSeqB 底漆与 600 μL HT1 缓冲液结合。涡旋和离心机。

4. 在测序墨盒的库井底部添加 20 μL 的稀释库。

5.  550 μL Read1 CustomSeqB 引物稀释加载到测序盒的 #1 井中。

6. 按照制造商的说明开始测序运行。

7. 为读取 1 选择自定义 1

8. 设置以下测序参数并运行:读取 126 bp,读取 282 bp,读取 1 索引:8 bp

 

Q. 生成 Drop-seq 核心计算协议所需的文件(见注 5)。

1. 使用以下命令生成序列字典:

 

java -jar /path/to/picard/picard.jar CreateSequenceDictionary \

参考=我的.fasta \

输出=我的.dict \

物种=物种名称

 

2. 使用以下命令生成 refFlat 注释文件:

 

/路径//Drop-seq_tools/ConvertToRefFlat \

ANNOTATIONS_FILE=我的.gtf \

SEQUENCE_DICTIONARY=我的.dict \

输出=我的.refFlat

 

3. 使用以下命令生成缩减的 GTF 文件:

 

/路径//Drop-seq_tools/ReduceGtf \

GTF=我的.gtf \

SEQUENCE_DICTIONARY=我的.dict \

输出=我的.reduced.gtf

 

4. 使用以下命令生成间隔文件:

 

/路径//Drop-seq_tools/CreateIntervalsFiles \

REDUCED_GTF=我的.reduced.gtf \

SEQUENCE_DICTIONARY=我的.dict \

前缀=我的\

输出=/路径//输出/文件\

MT_SEQUENCE=chrM

 

5. 使用以下命令为比对过程生成基因组目录:

 

/路径///\

--runMode 基因组生成\

--运行线程N 8 \

--genomeDir 路径//输出/文件\

--genomeFastaFiles 路径//FASTA/文件\

--sjdbGTFfile 路径//GTF/文件\

--sjdb悬垂81

 

R. 用于生成数字基因表达表的数据处理管道。

1. 使用以下命令解复用 i7 索引:

 

bcl2fastq -runfolder-dir /path/to/rawdata/folder/ \

-输出目录/路径//输出/文件夹/\

--无车道分割\

--加载线程 8 \

--写作线程 8 \

--minimum-trimmed-read-length 0 \

--mask-short-adapter-reads 0 \

--sample-sheet /path/to/sample/sheet/

 

2. 使用以下命令评估所有文件的测序数据质量:

 

/path/to/fastqc *.fastq.gz

 

有关如何解释 FastQC 质量指标结果的信息,请访问https://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/

3. 对于每个 i7-index 样本,使用以下命令将 Fastq 文件转换为 Sam 文件,同时合并 R1 R2 文件:

 

java -jar /path/to/picard/picard.jar CreateSequenceDictionary \

F1= SampleX_R1.fastq.gz \

F2= SampleX_R2.fastq.gz \

O= SampleX.bam

SM=SampleX

 

4. 使用以下命令提取每次读取的 RT 索引序列:

 

/path/to/Drop-seq_tools/TagBamWithReadSequenceExtended \

输入=SampleX.bam \

输出=索引_SampleX.bam \

摘要= Indexed_SampleX.summary \

基本范围= 9-16 \

基础质量=10 \

DISCARD_READ=\

TAG_NAME=XC\

NUM_BASES_BELOW_QUALITY=1

 

5. 使用以下命令提取每次读取的分子条形码序列:

 

/path/to/Drop-seq_tools/TagBamWithReadSequenceExtended \

输入=索引_SampleX.bam \

输出=UMIed_SampleX.bam \

摘要= UMIed_SampleX.summary \

基本范围= 1-8 \

基础质量=10 \

DISCARD_READ=\

TAG_NAME=XM\

NUM_BASES_BELOW_QUALITY=1

 

6. 使用以下命令删除具有低质量 RT 索引或分子条形码序列的读取:

 

/path/to/Drop-seq_tools/FilterBam \

TAG_REJECT=XQ\

输入=UMIed_SampleX.bam \

OUTPUT=Filtered_SampleX.bam

 

7. 使用以下命令修剪包含部分模板开关寡核苷酸的读取:

 

/path/to/Drop-seq_tools/TrimStartingSequence \

输入=Filtered_SampleX.bam \

输出= Trimmed_SampleX.bam \

OUTPUT_SUMMARY= Trimmed_SampleX.summary \

序列= AAGCAGTGGTATCAACGCAGAGTGAATGGG \

不匹配=0 \

NUM_BASES=5

 

8. 使用以下命令在读取中修剪 polyA 尾部:

 

/path/to/Drop-seq_tools/PolyATrimmer \

输入=修剪_SampleX.bam \

输出= PolyATrimmed_SampleX.bam \

OUTPUT_SUMMARY= PolyATrimmed_SampleX.summary \

序列= AAGCAGTGGTATCAACGCAGAGTGAATGGG \

不匹配=0 \

NUM_BASES=6 \

USE_NEW_TRIMMER =

 

9. 使用以下命令将 bam 文件转换回 Fastq 格式:

 

java jar /path/to/picard/picard.jar SamToFastq \

输入=PolyATrimmed_SampleX.bam \

FASTQ= PolyATrimmed_SampleX.fastq

 

10. 使用以下命令将读取与基因组对齐:

 

/路径///\

--runMode 对齐读取\

--runTreadN 8 \

--genomeDir 路径//基因组/文件夹/\

--readFilesIn PolyATrimmed_SampleX.fastq \

--outSAMtype BAM \

--按坐标排序\

--alignSoftClipAtReferenceEnds \

--outFilterScoreMinOverLread 0.66 \

--outFilterMatchNminOverLread 0.66

 

11. 使用以下命令计算 RNA 测序的质量指标:

 

java jar /path/to/picard/picard.jar CollectRNASeqMetrics \

I=Aligned_SampleX.bam \

O= SampleX.RNA_Metrics \

REF_FLAT = 我的.refFlat \

STRAND=FIRST_READ_TRANSCRIPTION_STRAND \

CHART_OUTPUT=SampleX_Metagene.plot \

RRNA_FRAGMENT_PERCENTAGE=0.8 \

MINIMUM_LENGTH=500 \

RIBOSOMAL_INTERVALS=/path/to/my.intervals/rRNA.intervals

 

(我们预计绝大多数碱基会映射到 mRNA,并且元基因图应该显示出强烈的 3' 偏差,如图 3 所示。)

 

 

3. scRNA-seq 数据 3' 偏差。

Metagene 图显示了整个 mRNA 长度上读数的预期分布(标准化覆盖率),其中 0 表示 5' 最远的区域,100 表示 3' 最远的区域。

 

12. 使用以下命令将对齐的 bam 文件与索引和 UMIed bam 文件合并:

 

java jar /path/to/picard/picard.jar MergeBamAlignment \

REFERENCE_SEQUENCE= /path/to/Genome/fasta \

UNMAPPED_BAM=UMIed_SampleX.bam \

ALIGNED_BAM= Aligned_SampleX.bam \

输出= Merged_SampleX.bam \

INCLUDE_SECONDARY_ALIGNMENTS=\

PAIRED_RUN =

 

13. 使用以下命令标记带有基因名称的读取:

 

/path/to/Drop-seq_tools/TagReadWithGeneFunction \

=Merged_SampleX.bam \

O= GeneTagged_SampleX.bam \

ANNOTATIONS_FILE= my.refFlat

 

14. 使用以下命令生成数字基因表达表:

 

/path/to/Drop-seq_tools/DigitalExpression \

=GeneTagged_SampleX.bam \

O=样品X.DGE.gz \

STRAND_STRATEGY=感觉\

摘要=SampleX.DGE.summary.txt \

CELL_BC_FILE=RT_Indexes*

 

*这是一个文本文件,逐行列出使用的所有索引的 8nt 序列。

 

数据分析


1. 使用此方法在大鼠原代海马神经元上生成的数据集可在登录代码 GSE157204 下的 NCBI 基因表达综合库中找到。从数据生成的数字表达式表和描述实验设计的元数据文件可在我们出版物的补充文件 1补充文件 2中找到 (Perez et al., 2021).用于数据分析的 R 源代码可以在我们的 GitHub 存储库DOI 中找到:10.5281/zenodo.4384479

2. 协议中包含的 ERCC Spike-Ins 可用于量化实验的准确性,方法是将单个 ERCC 的输入数量与其在排序结果中的平均数量进行比较。预计 Pearson 相关 > 0.8。此外,可以通过确定具有不同输入值的 ERCC 的检测概率来计算实验的灵敏度(检测到裂解反应中存在的 mRNA 的数量)。平均而言,我们检测到每 4 个分子中就有 1 个存在。

3. 在分析数字基因表达表之前,需要进行额外的质量评估和数据清理。首先,应使用 ERCC 来评估文库制备和测序的质量:应丢弃几乎没有 ERCC 测序的异常值。其次,具有与空切相当的 RNA 分子数量的体细胞或树突样品也应丢弃。最后,如下所述,无监督维度方法可以揭示数据集中存在的细胞类型。根据我们的经验,这可能会揭示富含凋亡或神经胶质细胞标记物的样本,这两种细胞都应该被丢弃。

4. 要将样本分类,请使用无监督的降维方法,例如 UMAP tSNE,以及最近邻方法,例如 k-clustering,所有这些方法都可以在 Seurat 包中找到 (Stuart et al ., 2019)。由于树突转录组通常比体细胞转录组更浅,我们仅根据体细胞样本进行细胞类型分类,然后将此信息外推到相应的树突。

5. 差异表达分析可以在不同细胞类型的胞体或树突之间进行,也可以在单个神经元的胞体和树突之间进行。对于这些,我们建议基于逻辑回归 (Ntranos et al ., 2019) 或泊松广义线性模型 (Stuart et al ., 2019) 进行测试。在比较体细胞与树突时,我们建议使用起源细胞作为潜在变量进行配对差异表达分析。

 

笔记

1. 在实验设计阶段,在选择合适的 N 时应考虑几个因素。功率分析应考虑最终将使用数据执行的测试和分析的类型,因为实现统计功效所需的 N 因测试而异测试。对于细胞类型之间或亚细胞区室之间的差异表达分析,我们建议使用针对单细胞数据集优化的工具,例如 powsimR (Vieth et al ., 2017) scPower (Schmid et al ., 2020)。在单次测序运行中可以分析的样本数量取决于实验装置的多路复用能力。在该协议中,RT 引物携带 16 个不同索引中的 1 个,而 i7 Nextera PCR 步骤将 24 个索引中的 1 个添加到之前的每个索引中,从而允许同时分析最多 (16 × 24) 384 个样本。对我们来说,这个数字为每个样本提供了足够的测序深度。但是,当然可以增加索引的数量,从而增加每次运行的多路复用能力。原则上,所有样品都可以来自单个板,但不建议这样做。由于 LCM 在室温下发生,并且一次只收集一个样本,因此 RNA 完整性会随着时间的推移而显着降低。我们观察到在收集 3 小时后每个样本检测到的 RNA 分子减少的趋势(表明 RNA 完整性较低)。因此,我们建议将每道菜收集的样本数量限制在可以在 3 小时内安全收集的数量(在我们的案例中为 48 个)。我们建议使用来自多个初级神经元制剂的菜肴,因为制剂之间会发生变异,因此来自同一制剂的菜肴不会产生完全独立的样本。

2. 除了来自相同单个神经元的配对体细胞和树突样本外,我们建议包括两种类型的对照。为了了解选择适合激光捕获的细胞所引入的潜在偏差,我们建议在同一培养皿中从不易接近的神经元中收集仅躯体样本。其次,作为阴性对照,我们包括将激光压力弹射应用于培养皿中没有胞体和/或树突的区域的样本。这些切口的大小应与胞体和/或树突占据的区域相当。这些样本用于控制可能污染的细胞外 RNA,并且可以帮助设置表达截止以包含样本。

3. 当使用磁珠纯化 DNA 时,重要的是在将磁珠与样品混合之前通过涡旋彻底重悬磁珠。在第 I-步骤 3 中准确移取所需体积也很重要,因为体积不准确会增加或减少较小 DNA 片段的存在。如果可能,请在此步骤中使用低绑定提示。同样重要的是要监测颗粒不会变得太干并在第 I-步骤 11 中开始出现裂缝,因为这将导致重新悬浮后 DNA 浓度降低。

4. 为了从 384 个收集的样本到对单个多路复用样本进行测序,我们在协议的关键步骤中逐步合并,即第 I 部分的步骤 2 17,以及第 N 部分的步骤 2。为了避免合并具有相同索引组合的样本,以及为了快速有效地执行该过程,我们建议根据图 4 所示的 96 孔板格式组织样品。

 

4. 样本池工作流程。

A. 在第 I 部分第 2 步中进行的合并。两个 96 孔板,每个板包含 48 个样品(仅显示 AD 行,因为 EH 行未被占用)。体细胞样本放置在第 135 7 列,它们各自的树突放置在第 246 8 列。来自不易接近区域的对照体细胞收集在第 910 11 列。 第 12 列包含空切(阴性对照)。板的每一行包含 16 种不同的 RT-UMI-Index 引物中的一种。在第 I 部分的第 2 步中,将两个互补板(包含一组不重叠的索引)的同一列中的所有样本合并在一起。 B. 在第 I 部分第 17 步中执行的合并。将同一列中的两个互补样本合并在一起以将样本数减少到 24 个。C.在第 N 部分第 2 步中执行合并。添加 Nextera 索引后,将所有样本合并在一起以将样本数减少到 1

 

5. P Q 部分中的命令将在执行分析的目录中的命令行界面中使用。

食谱

 

1. 细胞裂解预混液(表 1

 

1.细胞裂解预混液

 

2. RT 预混液(表 2

 

2. RT 预混液

 

3. PCR 前置放大器预混液(表 3

 

3. PCR PreAmp Master Mix

 

4. 最终 PCR 预混液(表 4

 

 

4.最终 PCR 预混液

 

1. 计算预混液体积以考虑移液错误。

2. 制备 100 × ERCC 溶液,稀释 ERCC RNA Spike-In Mix 1, 1:200,000

3. RT master mix 试剂的最终浓度基于 6.4 µl的最终体积3 µl细胞裂解混合物 + 3.4 µl RT mix)计算。

4. PCR PreAmp master mix 试剂的最终浓度基于 14 µl的最终体积6.4 µl RT 反应 + 7.6 µl PCR PreAmp mix)计算。

5. Final PCR master mix 试剂的最终浓度基于 50 µl的最终体积26 µl Tagmentation 反应 + 24 µl Final PCR mix)计算。

 

致谢

 

这项工作得到了马克斯普朗克学会的支持,以及欧洲研究委员会的高级研究员奖(赠款 743216)、DFG CRC 1080:神经稳态的分子和细胞机制,以及 DFG CRC 902:基于 RNA 的调节的分子原理。我们感谢 Ivy CW Susanne Tom Dieck 博士。 Chan 和舒曼实验室的现任和前任成员进行了有益的讨论和建议。该协议源自我们之前的工作Perez等人2021DOI10.7554/eLife.63092

 

利益争夺

 

作者宣称没有利益冲突。

 

伦理

 

涉及动物护理的程序按照符合国家和国际法律和政策(DIRECTIVE2010/63/EU;德国动物福利法,FELASA 指南)的机构指南进行,并经当地政府监督批准并报告当局(Regierungsprasidium Darmstadt)。 根据 2 Abs 的附件 2 对动物实施安乐死。 2 Tierschutz-Versuchstier-Verordnung 

 

 

 

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引用: Readers should cite both the Bio-protocol article and the original research article where this protocol was used:
  1. Perez, J. D. and Schuman, E. M. (2022). Subcellular RNA-seq for the Analysis of the Dendritic and Somatic Transcriptomes of Single Neurons. Bio-protocol 12(1): e4278. DOI: 10.21769/BioProtoc.4278.
  2. Perez, J. D., Dieck, S. T., Alvarez-Castelao, B., Tushev, G., Chan, I. C. and Schuman, E. M. (2021). Subcellular sequencing of single neurons reveals the dendritic transcriptome of GABAergic interneurons. Elife 10: e63092.
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