This work was supported by a grant from AMED under Grant Number JP18fk0108011 (KO and SI) and JP18fm0208002 (TS, KO, and YO), and a Grant-in-Aid from the Ministry of Education, Culture, Sports, Science and Technology (15K15159) to KO. We thank Sayuri Yamaguchi and Satoko Sasaki for the valuable technical assistance. We would like to thank Editage (www.editage.jp) for English language editing.

All animal experiments were performed according to institutional guidelines and with the approval of the National Institute of Infectious Diseases Animal Care and Use Committee. Sampling of PBMCs from healthy adult volunteers, used as the representative result in this report, was performed with the approval of the Ethics Committee of the National Institute of Infectious Diseases, Tokyo, Japan, and written informed consent was obtained from each participant using an ethics committee-approved form.
1. Primer Design
<ol>
	<li>Design a universal forward primer to cDNA to amplify the immunoglobulin mRNA without bias from PCR primers, as used in the 5&#39;-RACE11,12 and SMART-PCR13 techniques.</li>
	<li>For the immunoglobulin VH gene amplification, design the immunoglobulin class-specific sequences in the constant region as reverse primers8,14 (Figure 1A). 
	NOTE: Multiplex tag sequences can be added to any of these primers to label the library molecules from different sample sources. Sequences for nested PCR can also be added, according to the manual of the kit used15.</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Universal forward primer</td>
			<td>5&#39;- AAGCAGTGGTATCAACGCAGAGT-3&#39;</td>
		</tr>
		<tr>
			<td colspan="2">Reverse primers for the mouse immunoglobulins (Ref.8)</td>
		</tr>
		<tr>
			<td>IgM_CH1:</td>
			<td>5&#39;- CACCAGATTCTTATCAGACAGGGGGCTCTC -3&#39;</td>
		</tr>
		<tr>
			<td>IgG1_CH1:</td>
			<td>5&#39;- CATCCCAGGGTCACCATGGAGTTAGTTTGG -3&#39;</td>
		</tr>
		<tr>
			<td>IgG2c_CH1:</td>
			<td>5&#39;- GTACCTCCACACACAGGGGCCAGTGGATAG -3&#39;</td>
		</tr>
		<tr>
			<td>IgG3_CH1:</td>
			<td>5&#39;-ATGTGTCACTGCAGCCAGGGACCAAGGGA-3&#39;</td>
		</tr>
		<tr>
			<td>IgA_CH1:</td>
			<td>5&#39;-GAATCAGGCAGCCGATTATCACGGGATCAC-3&#39;</td>
		</tr>
		<tr>
			<td>Ig&#954;_CH1:</td>
			<td>5&#39;- GCTCACTGGATGGTGGGAAGATGGATACAG -3&#39;</td>
		</tr>
		<tr>
			<td>Ig&#955;_CH1:</td>
			<td>5&#39;- CTBGAGCTCYTCAGRGGAAGGTGGAAACA -3&#39;</td>
		</tr>
		<tr>
			<td colspan="2">Reverse primers for the human immunoglobulins (Ref.14)</td>
		</tr>
		<tr>
			<td>IgM_CH1:</td>
			<td>5&#39;- GGGAATTCTCACAGGAGACG -3&#39;</td>
		</tr>
		<tr>
			<td>IgG_CH1:</td>
			<td>5&#39;- AAGACCGATGGGCCCTTG -3&#39;</td>
		</tr>
		<tr>
			<td>IgD_CH1:</td>
			<td>5&#39;- GGGTGTCTGCACCCTGATA -3&#39;</td>
		</tr>
		<tr>
			<td>IgA_CH1:</td>
			<td>5&#39;- GAAGACCTTGGGGCTGGT -3&#39;</td>
		</tr>
		<tr>
			<td>IgE1_CH1:</td>
			<td>5&#39;- GAAGACGGATGGGCTCTGT -3&#39;</td>
		</tr>
		<tr>
			<td>IgE2_CH1:</td>
			<td>5&#39;- TTGCAGCAGCGGGTCAAGGG -3&#39;</td>
		</tr>
		<tr>
			<td>Ig&#954;_CH1:</td>
			<td>5&#39;- TGCTCATCAGATGGCGGGAAGAT -3&#39;</td>
		</tr>
		<tr>
			<td>Ig&#955;_CH1:</td>
			<td>5&#39;- AGAGGAGGGCGGGAACAGAGTGA -3&#39;</td>
		</tr>
	</tbody>
</table>
Table 1: Primer sequences for PCR-amplification of immunoglobulins
2. Nucleic Acid Isolation from Immune Cells and Tissues
NOTE: The procedure given below is for extracting nucleic acids from the mouse spleen. However, it is applicable to other immune tissues and human cells such as lymph nodes or peripheral blood mononuclear cells (PBMCs) (Figure 1B).
<ol>
	<li>Dissect the tissue, e.g., spleen from an 8-week-old C57BL/6 mouse and pass it through a stainless-steel mesh (200 to 400 &#181;m) with 2 mL of PBS buffer to obtain dispersed cells. Transfer the cell suspension to a 2.0 mL microcentrifuge tube, and centrifuge for 5 min at 600 &#215; g and 4 &#730;C. Discard the supernatant.</li>
	<li>Add 800 &#181;L of ACK lysing buffer (150 mM NH4Cl, 1 mM KHCO3, 0.1 mM Na2EDTA, pH 7.2) to the pellet, and incubate on ice for 2 min to lyse red blood cells in the tissue.</li>
	<li>Wash the tissue cells with 2 mL of PBS 3x, followed by centrifugation for 5 min at 600 &#215; g and 4 &#730;C.</li>
	<li>Add 800 &#181;L of phenol/guanidine isothiocyanate reagent to the pellet, vortex thoroughly, and incubate at about 25 &#730;C for 5 min.</li>
	<li>Add chloroform (200 &#181;L), shake manually for 15 s, and then incubate for 2 min at about 25 &#730;C.</li>
	<li>Separate the phases by centrifugation for 15 min at 12,000 &#215; g and 25 &#730;C and transfer the upper aqueous phase to a fresh tube.</li>
	<li>Add one volume of 70% ethanol, vortex briefly and apply it to the silica spin column.</li>
	<li>Elute the RNA with 30&#8211;100 &#181;L of water.</li>
	<li>Quantitate initial RNA concentration using a fluorometer (Table of Materials).</li>
	<li>Store the purified RNA at -80 &#730;C.</li>
</ol>
3. cDNA Synthesis and PCR Amplification
NOTE: The method described below is based on the 5&#39;-RACE11,12 and SMART-PCR techniques13. The details and optimization of the reaction are described in the manual of the kit 15. The starting materials for mouse immunoglobulin are the sample from step 2.10. The starting materials for human immunoglobulin are the sample from human tissues, ex. PBMC, treated as described in steps 2.3 to 2.10.
<ol>
	<li>Synthesize the first-strand cDNA from 2 to 10 &#181;g of total RNA template using 5&#39;-RACE CDS primer (oligo-dT-containing) and SMART-PCR oligonucleotide (Table of Materials) according to the manufacturer&#8217;s instructions15.
	<ol>
		<li>For the mouse immunoglobulin, PCR-amplify cDNA with high-fidelity DNA Polymerase using the universal forward primer and immunoglobulin class-specific reverse primers (Table 1). Set the thermal cycling conditions as: 94 &#730;C for 2 min, then 40 cycles of 94 &#730;C for 30 s, 59 &#730;C for 30 s, and 72 &#730;C for 30 s, followed by a final extension step at 72 &#730;C for 5 min. 
		NOTE: Typical experiments amplify IgM, IgG1, IgG2c, Igk and Igl immunoglobulin classes to look at the naive, Th1-dependent and Th2-dependent B cells (Figure 3).</li>
		<li>For the human immunoglobulin, perform the 1st PCR using the universal forward primer and immunoglobulin class-specific reverse primers (Table 1) with tag sequences. Include the index sequences for each sample by 2nd PCR using index sequence primers. Use the following PCR conditions and the Taq polymerase: 94 &#730;C for 2 min, 21 cycles (1st PCR) or 32 cycles (2nd PCR) at 94 &#730;C for 30 s, 59 &#730;C for 30 s, 72 &#730;C for 30 s. 
		NOTE: Typical experiments amplify IgM, IgD, IgG (IgG1, IgG2, IgG3 and IgG4), IgA (IgA1 and IgA2), IgE, Igk and Igl immunoglobulin classes to look at all B cell populations (Figure 4).</li>
	</ol>
	</li>
	<li>Electrophorese the PCR products on an agarose gel and purify 600 to 800 bp fragments using a silica membrane spin-column.
	<ol>
		<li>Electrophorese the sample from 3.2.1 or 3.2.2 on 2% agarose gel.</li>
		<li>Visualize the DNA bands on UV-transilluminator and excise the gel-slice containing the broad band between 600 to 800 bp.</li>
		<li>Add 10 &#956;L of membrane binding solution per 10 mg of gel slice. Mix and incubate at 50&#8211;65 &#176;C until the gel slice is completely dissolved.</li>
		<li>Transfer the gel solution on silica membrane spin-column. Wash once with washing buffer and elute DNA with 50 &#956;L of nuclease-free water (Table of Materials).</li>
	</ol>
	</li>
	<li>Quantify the purified amplicons with a fluorometer and pool amplicons from each immunoglobulin class in equal amounts for NGS sequencing. 
	NOTE: Typically, 2-10 &#956;g amplicon DNA was recovered for each immunoglobulin class. Mix each sample solution equally in DNA amount to give rise 50 &#956;L solution containing 10-20 ng DNA/&#956;L.</li>
	<li>Determine the size and concentration of libraries using a micro-capillary based electrophoresis with DNA sizing chip (Table of Materials). Store the libraries at - 20 &#176;C.</li>
</ol>
4. NGS Sequencing of Libraries
<ol>
	<li>Generate a SampleSheet.cvs for the sequencing run specifying sample name, index information and instruct to obtain .fastq files only.</li>
	<li>Thaw the reagent cartridge (Table of Materials) and the libraries.</li>
	<li>Make 0.2 N NaOH and dilute the libraries to obtain the desired molar concentration.</li>
	<li>Rinse and dry the flow cell. Add 600 &#956;L of diluted and denatured library solution into the well of the reagent cartridge.</li>
	<li>Start the sequencing run.</li>
</ol>
5. Quality Control of NGS Data
<ol>
	<li>Perform the quality control of FASTQ data using the &#34;FASTX-Toolkit&#34;16. 
	NOTE: A basic example of the parameter settings used is as follows: 
	fastq_quality_trimmer -v -t 20 -l 200 -i [InFilename.fastq] -o [InFilename.fastq] 
	fastq_quality_filter -v -q 20 -p 80 -i [InFilename.fastq] -o [InFilename.fastq] 
	fastx_reverse_complement -v -i [InFilename.fastq] -o [InFilename.fastq]</li>
	<li>Format the output files to &#34;fasta nucleic acid (.fna)&#34; by the following command: 
	fastq_to_fasta -v -n -i [InFilename.fastq] -o [InFilename.fna]</li>
</ol>
6. Extraction and Analysis of Immunoglobulin Sequences from .fna Data
NOTE: The example programs were implemented in a UNIX environment. Please use them as an example references because performance may depend on the operating system and hardware environment. The authors do not accept any liability for errors or omissions. The programming languages, Perl17, R18, and required modules need to be installed according to the instructions on the cited websites. the IgBLAST program need to be installed according to the instructions on the appropriate website19,20.
<ol>
	<li>Download the following examples of in-house programs for repertoire analyses from https://github.com/KzPipeLine/KzPipeLine: 
	03_PipeLine_Mouse.zip; A set of example programs for the analyses of mouse antibody sequences. 
	05_PipeLine_Human.zip; A set of example programs for the analyses of human antibody sequences.</li>
	<li>Extract the antibody reads in the sequence data: Extract the immunoglobulin (Ig) sequences of each Ig-class from the data (.fna ) by a Perl program that searches the signature sequences in each immunoglobulin constant region (Table 2).
	<ol>
		<li>For the mouse immunoglobulin heavy chain (IgH) genes, extract the reads by the following command: 
		&#8203;$ perl 01_KzMFTIgCmgggaNtdVer3_Kz160607.pl [Input filename] [Output filename (suffix)]</li>
		<li>For the mouse immunoglobulin light chain (IgL) genes extract the reads by the following command: 
		&#8203;$ perl 01_KzMFTCkltNtdVer1_170810.pl [Input filename] [Output filename (suffix)]</li>
		<li>For the human immunoglobulin heavy chain (IgH) genes, extract the reads by the following command: 
		&#8203;$ perl 01_KzMfHuIgHCmgadeNtdVer1_Kz180312.pl [Input filename] [Output filename (suffix)]</li>
		<li>For the human immunoglobulin light chain (IgL) genes, extract the reads by the following command: 
		$ perl 01_KzMfHuIgCkltNtd_180316.pl [Input filename] [Output filename (suffix)]</li>
	</ol>
	</li>
	<li>Annotate and check the productivity of V(D)J gene recombination: 
	NOTE: The method described below utilizes standalone IgBLAST19 for the annotation of V(D)J gene segments in the sequence. Set the database for the V(D)J genes and the parameter settings for IgBLAST as described20.
	<ol>
		<li>Annotate the mouse immunoglobulin heavy chain (IgH) genes by the following command: 
		$ igblastn -germline_db_V $IGDATA/ImtgMouseIghV_NtdDb.txt -germline_db_J $IGDATA/ImtgMouseIghJ_NtdDb.txt -germline_db_D $IGDATA/ImtgMouseIghD_NtdDb.txt -organism mouse -domain_system imgt -query ./$InFile -auxiliary_data $IGDATA/optional_file/mouse_gl.aux -show_translation -outfmt 7 &#62;&#62; ./$OutName</li>
		<li>Annotate the mouse immunoglobulin light chain (IgL) genes by the following command: 
		$ igblastn -germline_db_V $IGDATA/ImtgMouseIgkV_NtdDb.txt -germline_db_J $IGDATA/ImtgMouseIgkJ_NtdDb.txt -germline_db_D $IGDATA/ImtgMouseIghD_NtdDb.txt -organism mouse -domain_system imgt -query ./$InFile -auxiliary_data $IGDATA/optional_file/mouse_gl.aux -show_translation -outfmt 7 &#62;&#62; ./$OutName</li>
		<li>Annotate the human immunoglobulin heavy chain (IgH) genes by the following command: 
		$ igblastn -germline_db_V $IGDATA/ImtgHumanIghV_NtdDb.txt -germline_db_J $IGDATA/ImtgHumanIghJ_NtdDb.txt -germline_db_D $IGDATA/ImtgHumanIghD_NtdDb.txt -organism human -domain_system imgt -query ./$InFile -auxiliary_data $IGDATA/optional_file/Human_gl.aux -show_translation -outfmt 7 &#62;&#62; ./$OutName</li>
		<li>Annotate the human immunoglobulin light chain (IgL) genes by the following command: 
		$ igblastn -germline_db_V $IGDATA/ImtgHumanIgkV_NtdDb.txt -germline_db_J $IGDATA/ImtgHumanIgkJ_NtdDb.txt -germline_db_D $IGDATA/ImtgHumanIghD_NtdDb.txt -organism human -domain_system imgt -query ./$InFile -auxiliary_data $IGDATA/optional_file/human_gl.aux -show_translation -outfmt 7 &#62;&#62; ./$OutName</li>
	</ol>
	</li>
	<li>Visualize the global feature of an antibody repertoire.
	<ol>
		<li>Visualize the mouse IgH repertoire by the following command: 
		$ . 00a1_3DView_MoIgH_Kz180406.sh 
		&#8203;NOTE: The input file is filename.fna (sequence data), preferably the output file from 6.2.1. This file needs to be placed in a lower directory (folder) named &#34;filename&#34;. In line 50 of the shell script, assign a &#34;filename&#34; for Para_4.</li>
		<li>Visualize the human IgH repertoire by the following command: 
		$ . 00a1_3DView_HuIgH_Kz180411.sh 
		&#8203;NOTE: The input file is filename.fna sequence data, preferably the output file of 6.2.3. This file needs to be placed in the lower directory (folder) that name is &#34;filename&#34;. In line 46 of the shell script, assign a &#34;filename&#34; for Para_4.</li>
		<li>Visualize the mouse IgL repertoire by the following command: 
		$ . 00_2DViewS_MoIgL_Kz180406.sh 
		&#8203;NOTE: With this pipeline, Igk and Igl are processed concomitantly. The input file is filename.fna (sequence data), preferably the output file from 6.2.2. This file needs to be placed in the lower directory (folder) named &#34;filename&#34;. In line 53 of the shell script, assign a &#34;filename&#34; for Para_4. The output file&#8217;s name, ending with &#34;_IgKlCount.txtDim2Rpm.txt&#34; gives the coordinates for a two-dimensional bar graph (Figure 3, IgL).</li>
		<li>Visualize the human IgL repertoire by the following command: 
		$ . 00_2DView_HuIgL_Kz180319.sh 
		NOTE: With this pipeline, Igk and Igl are processed concomitantly. The input file is filename.fna sequence data, preferably the output file of 6.2.4. This file needs to be placed in a lower directory (folder) named &#34;filename&#34;. In line 53 of the shell script, assign &#34;filename&#34; for Para_4. The output file name ending with &#34;_IgKlCount.txtDim2Rpm.txt&#34; gives the coordinates for a two-dimensional bar graph (Figure 4, IgL).</li>
	</ol>
	</li>
</ol>

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P., Giudicelli, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=IMGT((R))+tools+for+the+nucleotide+analysis+of+immunoglobulin+(IG)+and+T+cell+receptor+(TR)+V-(D)-J+repertoires,+polymorphisms,+and+IG+mutations:+IMGT/V-QUEST+and+IMGT/HighV-QUEST+for+NGS.">IMGT((R)) tools for the nucleotide analysis of immunoglobulin (IG) and T cell receptor (TR) V-(D)-J repertoires, polymorphisms, and IG mutations: IMGT/V-QUEST and IMGT/HighV-QUEST for NGS.</a> Methods in Molecular Biology. 882, 569-604 (2012).</li><li>Glanville, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Precise+determination+of+the+diversity+of+a+combinatorial+antibody+library+gives+insight+into+the+human+immunoglobulin+repertoire.">Precise determination of the diversity of a combinatorial antibody library gives insight into the human immunoglobulin repertoire.</a> Proceedings of the National Academy of Sciences of the United States of America. 106 (48), 20216-20221 (2009).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> rgl: 3D Visualization Using OpenGL. , (2015).</li><li>Hardy, R. R., Wei, C. J., Hayakawa, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Selection+during+development+of+VH11++B+cells:+a+model+for+natural+autoantibody-producing+CD5++B+cells.">Selection during development of VH11+ B cells: a model for natural autoantibody-producing CD5+ B cells.</a> Immunological Reviews. , 60-74 (2004).</li></ol>

The authors have no conflicts of interest to disclose.

The method described here utilizes NGS for antibody RNA amplified using the 5&#39;-RACE method. In contrast to methods that use degenerate 5&#39;-VH gene primers, mRNAs of each antibody class are amplified evenly using universal forward primers. In addition, the use of antisense primers specific for the constant-region 1 (CH1) of the antibody gene enables repertoire profiling of specific immunoglobulin classes. This is very beneficial for dissecting the class-specific antibody response, as well as for comparing naive and immunized repertoires8,9.
A most likely pitfall of the method is a paucity of amplified immunoglobulin messages. The depth of antibody repertoire obtained by this protocol substantially depends on the PCR amplification described in steps 3.1 and 3.2. If the repertoire depth is not properly obtained, changing the ratios of template cDNA and primers in steps 3.2.1 or 3.2.2 is strongly recommended.
Generally, approximately 20% of the antibody reads produced by NGS are ambiguous sequences21. Even with established &#34;correction methods&#34;, 5-10% remain ambiguous3. We, therefore, analyzed the sequence and filtered raw reads containing signature sequences corresponding to immunoglobulin constant regions (C&#956;H1, C&#947;1H1, C&#947;2cH1, etc.). Hence the analysis of somatic hyper-mutations needs the careful examinations.
One of the limitations of this method is that immunoglobulin heavy and light chain pair is unable to be inferred. Hence the repertoire view obtained by this method is not holistic. However, it is possible to approximate the top-ranking pairs by statistical analysis of the data10. Also, a novel method to sequence the immunoglobulin pairs was reported recently3,4.
The immunoglobulin sequences in the output .fna data were extracted based on the presence of immunoglobulin gene signature sequences. The V, D, and J gene segments were then annotated and the productivity of V(D)J rearrangements were assessed. The complementarity-determining region 3 (CDR3) sequences were also annotated. These systematic examinations of immunoglobulin sequences in .fna data were usefully provided by the IMGT/HighV-QUEST server22,23,24. However, building an automated processing pipeline has the merit to analyze the big experimental data. The pipeline customized for each purpose is possible to set up by using the standalone IgBLAST protocol19. This approach needs basic programming literacy but is very useful for detailed analyses of the immunoglobulin system. The pipelines described are the examples of the customized protocol (Figure 2).
The number of antibody reads is proportional to the amount of antibody RNAs in the sample, reflecting the antibody constituents of the antibody system at given time points5,8,25. The method described here gives a bird&#39;s eye view of the V(D)J constitution of an antibody repertoire using R programs8,18,26.
The global view of IgM antibody repertoires of individual naive mice revealed a highly conserved VDJ-profile as compared to those of IgG1 or IgG2c8. It was reported that VDJ combinations of immature zebrafish are highly stereotyped9. In contrast, human VDJ combinations are reported to be highly skewed6. The highly conserved deterministic VDJ-profiles in naive B cells are probably generated either by skewed VDJ-rearrangements or negative selection with auto-antigens presented in the body. For example, IGHV11-2 is expressed preferentially in the fetal IgM repertoire27 and this predominance is attributed to the autoreactivity of IGHV11-2 against senescent erythrocytes27. Interestingly, IGHV11-2 was also the most common major repertoire in our previously published analysis of naive IgM8.
The method described here is useful for deciphering antigen-responsive antibody repertoires by inclusively analyzing the antibody-repertoire space generated in individual bodies, avoiding inadvertent omission of key antibody repertoires8,10. This method also allows the examination of detailed antibody network dynamism, which would facilitate accelerated discovery of protective antibodies against newly emerging pathogens.

The antibody system is one of the fundamentals of the acquired immune system. It is highly potent against invading pathogens due to its vast diversity, fine antigen recognition specificity, and the clonal expansion of antigen-specific B cells. The repertoire of antibody-producing B cells is estimated to be more than 1015 in a single individual1. This immense diversity is generated with the help of VDJ gene recombination in the immunoglobulin genetic loci2. Description of the entire B cell repertoires and their dynamic changes in response to antigen-immunization is therefore challenging, but essential for a complete understanding of the antibody response against invading pathogens.
Because of their astronomical diversity, B cell repertoires have been regarded as a black box; however, the advent of NGS technology has enabled breakthroughs to an enhanced understanding of their complexity3,4. Whole antibody repertoires have been successfully analyzed, firstly in zebrafish5, then mice6, and humans6,7. Although NGS has now become a powerful tool in the study of the adaptive immune response, basic analyses of the commonalities and differences in antibody repertoires among individual animals are lacking.
In mice, it was reported that the IgM repertoires are almost identical between individuals, whereas those of IgG1 and IgG2c are substantially different between individuals8. In addition to V-gene usage profile, the observed frequency of VDJ-profile in naive peripheral B cells is highly similar between individuals8. The analysis of the amino acid sequences of the VDJ-region also showed the occurrence of the same junctional sequences in different mice much more frequently than previously thought8. These results indicate that the mechanisms for the antibody repertoire formation can be deterministic rather than stochastic5,8,9. The process of antibody repertoire development in mice has also been successfully analyzed using NGS to further highlight the potential of NGS to uncover the antibody immune system in detail10.
In this report, we describe a simple and efficient method to visualize and analyze an antibody repertoire at a global level.

<table><tbody><tr><td>0.2 mL Strip Tubes</td><td>Thermo Fisher Scientific</td><td>AB0452</td><td>120 strips</td></tr><tr><td>100 bp DNA Ladder</td><td>TOYOBO</td><td>DNA-035</td><td>0.5 mL</td></tr><tr><td>2100 Bioanalyzer Systems</td><td>Agilent Technologies</td><td>G2939BA /2100</td><td></td></tr><tr><td>Acetic Acid</td><td>Wako</td><td>017-00256</td><td>500 mL</td></tr><tr><td>Agarose, NuSieve GTG</td><td>Lonza</td><td>50084</td><td></td></tr><tr><td>Ammonium Chloride</td><td>Wako</td><td>017-02995</td><td>500 g</td></tr><tr><td>Chloroform</td><td>Wako</td><td>038-02606</td><td>500 mL</td></tr><tr><td>Dulbecco&#039;s PBS (-)&amp;ldquo;Nissui&amp;rdquo;</td><td>NISSUI&amp;nbsp;</td><td>08192</td><td></td></tr><tr><td>Ethylenediamine-N,N,N&#039;,N&#039;-tetraacetic Acid Disodium Salt Dihydrate (2NA)</td><td>Wako</td><td>345-01865</td><td>500 g</td></tr><tr><td>Falcon 40 &amp;micro;m Cell Strainer</td><td>Falcon</td><td>352340</td><td>50/Case</td></tr><tr><td>ling lock tube 1.7 mL</td><td>BM EQUIPMENT</td><td>BM-15</td><td></td></tr><tr><td>ling lock tube 2.0 mL</td><td>BM EQUIPMENT</td><td>BM-20</td><td></td></tr><tr><td>MiSeq Reagent Kit v2</td><td>illumina</td><td>MS-102-2003</td><td>500 cycles</td></tr><tr><td>MiSeq System</td><td>illumina</td><td>SY-410-1003</td><td></td></tr><tr><td>NanoDrop 2000c Spectrophotometer&amp;nbsp;</td><td>Thermo Fisher Scientific</td><td></td><td></td></tr><tr><td>Potassium Hydrogen Carbonate</td><td>Wako</td><td>166-03275</td><td>500 g</td></tr><tr><td>PureLink RNA Mini Kit</td><td>life technologies</td><td>12183018A</td><td></td></tr><tr><td>Qubit 3.0 Fluorometer</td><td>Thermo Fisher Scientific</td><td>Q33216</td><td></td></tr><tr><td>Qubit dsDNA HS Assay Kit</td><td>Thermo Fisher Scientific</td><td>Q32854</td><td>500 assays</td></tr><tr><td>SMARTer RACE 5&amp;rsquo;/3&amp;rsquo; Kit&amp;nbsp;</td><td>Clontech&amp;nbsp;</td><td>634858</td><td></td></tr><tr><td>TaKaRa Ex Taq Hot Start Version&amp;nbsp;</td><td>Takara Bio Inc.&amp;nbsp;</td><td>RR006A&amp;nbsp;</td><td></td></tr><tr><td>Trizma base</td><td>Sigma</td><td>T6066</td><td>1 kg</td></tr><tr><td>TRIzol Reagent</td><td>AmbionThermo Fisher Scientific</td><td>15596026</td><td>100 mL</td></tr><tr><td>Ultra Clear qPCR Caps</td><td>Thermo Fisher Scientific</td><td>AB0866</td><td>120 strips</td></tr><tr><td>UltraPure Ethidium Bromide</td><td>Thermo Fisher Scientific</td><td>15585011</td><td></td></tr><tr><td>Wizard SV Gel and PCR Clean-Up System&amp;nbsp;</td><td>Promega&amp;nbsp;</td><td>A9282</td><td></td></tr></tbody></table>

identification of mouse and human antibody repertoires by next-generation sequencing

The immense adaptability of antigen recognition by antibodies is the basis of the acquired immune system. Despite our understanding of the molecular mechanisms underlying the production of the vast repertoire of antibodies by the acquired immune systems, it has not yet been possible to arrive at a global view of a complete antibody repertoire. In particular, B cell repertoires have been regarded as a black box because of their astronomical number of antibody clones. However, next-generation sequencing technologies are enabling breakthroughs to increase our understanding of the B cell repertoire. In this report, we describe a simple and efficient method to visualize and analyze whole individual mouse and human antibody repertoires. From the immune organs, representatively from spleen in mice and peripheral blood mononuclear cells in humans, total RNA was prepared, reverse transcribed, and amplified using the 5&#39;-RACE method. Using a universal forward primer and antisense primers for the antibody class-specific constant domains, antibody mRNAs were uniformly amplified in proportions reflecting their frequencies in the antibody populations. The amplicons were sequenced by next-generation sequencing (NGS), yielding more than 105 antibody sequences per immunological sample. We describe the protocols for antibody sequence analyses including V(D)J-gene-segment annotation, a bird&#39;s-eye view of the antibody repertoire, and our computational methods.

Antibody repertoires of mouse
A perspective of a murine antibody repertoire as a whole can be obtained from cells or tissues such as the spleen, bone marrow, lymph node, or blood. Figure 3 shows representative results of IgM, IgG1, IgG2c, and immunoglobulin light chain (IgL) repertoires from a na&#239;ve mouse spleen. The summary of the read numbers is shown in Table 3. For example, 166,175/475,144 reads contained IgM-specific signature sequence (Table 2) and 133,371/166,175 reads were VDJ-productive inferred by IgBLAST19.
Figure 3 shows a repertoire profile of VDJ-rearrangement by 3D-VDJ-plot, in which the size of each ball represents the relative number of reads; in other words, the number of antibody mRNAs in whole B cells. The 3-D mesh consists of 110 IGHV, 12 IGHD, and 4 IGHJ, which are aligned to reflect their order on the chromosome. In addition, the genes ambiguously assigned by IgBLAST were collected separately in the last position for each IGHV, IGHD and IGHJ line, giving rise to 7,215 nodes in the cuboid.
Also, shown in Figure 3 is a 2D-VJ-plot showing the profile of VJ-rearrangement in the IgL repertoire. The length of each bar on this plot represents the relative number of reads. The x-axis represents 101 IGLV&#954; and 3 IGLV&#955; genes, and the y-axis represents 4 IGLJ&#954; and 3 IGLJ&#955; genes. The unannotated V- and J-genes are represented on the right borderline.
The complementarity-determining region 3 (CDR3) sequences of these productive reads, which give rise to the majority of antigen-binding specificity, are given in IgBLAST outputs. The CDR3 sequences can be analyzed statistically, including biological or technical replicates, as described previously8,10.
Human antibody repertoires
A perspective of a human antibody repertoire as a whole can be analyzed from various tissues including peripheral blood mononuclear cells (PBMCs) or pathological tissues. Figure 4 shows representative results of IgM, total IgG (IgG1, IgG2, IgG3, and IgG4), total IgA (IgA1 and IgA2), IgD, IgE and IgL repertoires from normal PBMCs. A summary of the read numbers is shown in Table 3. For example, 90,238/1,582,754 reads contained IgM-specific signature sequence and 67,896/90,238 reads were VDJ-productive.
The repertoire profile of VDJ rearrangement is shown on a 3D-VDJ-plot in which the size of each ball represents the relative number of reads; in other words, the number of antibody mRNAs from whole PBMCs (Figure 4). The 3-D mesh consists of 56 IGHV, 27 IGHD, and 6 IGHJ, aligned in the order they appear on the chromosome. In addition, genes ambiguously assigned by IgBLAST are represented separately in the last position for each IGHV, IGHD and IGHJ line, giving rise to 11,172 nodes in the cuboid.
The profile of VJ-rearrangement in the IgL repertoire is depicted in a 2D-VJ-plot in which the length of each bar represents the relative number of reads (Figure 4). The x-axis represents 41 IGLV&#954; and 32 IGLV&#955; genes, and the y-axis represents 5 IGLJ&#954; and 5 IGLJ&#955; genes. The un-annotated V- and J-genes are represented on the right borderline.
The human CDR3 sequences are given in IgBLAST outputs and can be analyzed statistically as described previously8,10.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Immunoglobulin class</td>
			<td>Sense</td>
			<td>Antisense</td>
		</tr>
		<tr>
			<td colspan="2">Mouse immunoglobulin heavy chains (C57BL/6)</td>
			<td></td>
		</tr>
		<tr>
			<td>IgM</td>
			<td>AGTCAGTCCTTCCCAAATGTC</td>
			<td>GACATTTGGGAAGGACTGACT</td>
		</tr>
		<tr>
			<td>IgG1</td>
			<td>AAAACGACACCCCCATCTGTC</td>
			<td>GACAGATGGGGGTGTCGTTTT</td>
		</tr>
		<tr>
			<td>(IgG1 variant)</td>
			<td>AAAACAACACCCCCATCAGTC</td>
			<td>GACTGATGGGGGTGTTGTTTT</td>
		</tr>
		<tr>
			<td>IgG2c</td>
			<td>AAAACAACAGCCCCATCGGTC</td>
			<td>GACCGATGGGGCTGTTGTTTT</td>
		</tr>
		<tr>
			<td>IgG3</td>
			<td>GTGATCCCGTGATAATCGGCT</td>
			<td>AGCCGATTATCACGGGATCAC</td>
		</tr>
		<tr>
			<td>IgA</td>
			<td>TCCCTTGGTCCCTGGCTGCAG</td>
			<td>TCCCTTGGTCCCTGGCTGCAG</td>
		</tr>
		<tr>
			<td colspan="2">Mouse immunoglobulin light chains (C57BL/6)</td>
			<td></td>
		</tr>
		<tr>
			<td>Ig&#954;</td>
			<td>CTGTATCCATCTTCCCACCATCCAGTGAGC</td>
			<td>GCTCACTGGATGGTGGGAAGATGGATACAG</td>
		</tr>
		<tr>
			<td>Ig&#955;1</td>
			<td>TGTTTCCACCTTCCTCTGAAGAGCTCGAG</td>
			<td>CTCGAGCTCTTCAGAGGAAGGTGGAAACA</td>
		</tr>
		<tr>
			<td>Ig&#955;2</td>
			<td>TGTTTCCACCTTCCTCTGAGGAGCTCAAG</td>
			<td>CTTGAGCTCCTCAGAGGAAGGTGGAAACA</td>
		</tr>
		<tr>
			<td>Ig&#955;3</td>
			<td>TGTTTCCACCTTCCCCTGAGGAGCTCCAG</td>
			<td>CTGGAGCTCCTCAGGGGAAGGTGGAAACA</td>
		</tr>
		<tr>
			<td>Ig&#955;4</td>
			<td>TGTTCCCACCTTCCTCTGAAGAGCTCAAG</td>
			<td>CTTGAGCTCTTCAGAGGAAGGTGGGAACA</td>
		</tr>
		<tr>
			<td colspan="2">Human immunoglobulin heavy chains</td>
			<td></td>
		</tr>
		<tr>
			<td>IgM</td>
			<td>GGGAGTGCATCCGCCCCAAC</td>
			<td>GTTGGGGCGGATGCACTCCC</td>
		</tr>
		<tr>
			<td>IgG</td>
			<td>GCTTCCACCAAGGGCCCATC</td>
			<td>GATGGGCCCTTGGTGGAAGC</td>
		</tr>
		<tr>
			<td>IgA</td>
			<td>GCATCCCCGACCAGCCCCAA</td>
			<td>GACCGATGGGGCTGTTGTTTT</td>
		</tr>
		<tr>
			<td>IgD</td>
			<td>GCACCCACCAAGGCTCCGGA</td>
			<td>TCCGGAGCCTTGGTGGGTGC</td>
		</tr>
		<tr>
			<td>IgE</td>
			<td>GCCTCCACACAGAGCCCATC</td>
			<td>GATGGGCTCTGTGTGGAGGC</td>
		</tr>
		<tr>
			<td colspan="2">Human immunoglobulin light chains</td>
			<td></td>
		</tr>
		<tr>
			<td>Ig&#954;</td>
			<td>ACTGTGGCTGCACCATCTGC</td>
			<td>GCAGATGGTGCAGCCACAGT</td>
		</tr>
		<tr>
			<td>Ig&#955;1,2,6</td>
			<td>GTCACTCTGTTCCCGCCCTC</td>
			<td>GAGGGCGGGAACAGAGTGAC</td>
		</tr>
		<tr>
			<td>Ig&#955;3,7</td>
			<td>GTCACTCTGTTCCCACCCTC</td>
			<td>GAGGGTGGGAACAGAGTGAC</td>
		</tr>
	</tbody>
</table>
Table 2: Summary of the immunoglobulin signature sequences

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Mouse IgH</td>
			<td>Total reads</td>
			<td>IgM</td>
			<td>IgG1</td>
			<td>IgG2c</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Input</td>
			<td>475,144</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>IgC-containing</td>
			<td></td>
			<td>166,175</td>
			<td>229,671</td>
			<td>36,628</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>VDJ-productive</td>
			<td></td>
			<td>133,371</td>
			<td>196,583</td>
			<td>31,446</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Mouse IgL</td>
			<td>Total reads</td>
			<td>IgKappa</td>
			<td>IgLambda</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Input</td>
			<td>527,668</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>IgC-containing</td>
			<td></td>
			<td>178,948</td>
			<td>21,446</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>VJ-productive</td>
			<td></td>
			<td>160,924</td>
			<td>16,988</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Human IgH</td>
			<td>Total reads</td>
			<td>IgM</td>
			<td>IgG</td>
			<td>IgA</td>
			<td>IgD</td>
			<td>IgE</td>
		</tr>
		<tr>
			<td>Input</td>
			<td>1,582,754</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>IgC-containing</td>
			<td></td>
			<td>90,238</td>
			<td>5,298</td>
			<td>94,061</td>
			<td>75,549</td>
			<td>2,932</td>
		</tr>
		<tr>
			<td>VDJ-productive</td>
			<td></td>
			<td>67,896</td>
			<td>2,775</td>
			<td>78,203</td>
			<td>56,495</td>
			<td>3</td>
		</tr>
		<tr>
			<td>Human IgL</td>
			<td>Total reads</td>
			<td>IgKappa</td>
			<td>IgLambda</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Input</td>
			<td>1,582,754</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>IgC-containing</td>
			<td></td>
			<td>120,316</td>
			<td>64,148</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>VJ-productive</td>
			<td></td>
			<td>97,169</td>
			<td>52,324</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 3: Summary of the read numbers in the experiments
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58804/58804fig1.jpg" /> 
Figure 1: Schematic representation of sequencing strategy for analyzing antibody repertoires in individual mice. (A) Total RNA from the immune cells or tissues was reverse-transcribed and PCR-amplified using the universal forward primer and immunoglobulin class-specific reverse primers. The amplicons from each immunoglobulin class were pooled and rendered for next-generation sequencing.(B) The biological replicates such as spleens from C57BL/6 mice were treated as follows: total RNAs were purified from spleen samples, and cDNAs were amplified by 5&#39;-RACE using the universal primer and antibody class-specific primer. They were then rendered for next-generation sequencing with labeling primers for individual mice. Parts of the figure are adapted from8 with permission. <a href="https://www.jove.com/files/ftp_upload/58804/58804fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58804/58804fig2.jpg" /> 
Figure 2: Schematic of data-processing flowchart for analyzing antibody repertoires in individual mice. Amplicon reads obtained after next-generation sequencing were processed as follows: (1) read sequences were checked for the presence of antibody class-specific signature sequences; (2) sequences were examined for the V, D, and J gene fragments using IMGT/HighV-Quest and/or IgBLAST; (3) the sequences containing a productive VDJ junction were collected; and (4) these sequences were used for the analysis of overall repertoire features, CDR3, etc. <a href="https://www.jove.com/files/ftp_upload/58804/58804fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58804/58804fig3.jpg" /> 
Figure 3: Global data visualization for mouse antibody repertoires. The overall repertoire profiles of each antibody class were visualized by 3D-VDJ-plot. The x-axis represents 110 IGHV genes ordered as on the chromosome. The y- and z-axis represents 12 IGHD and 4 IGHJ genes, respectively. The volume of spheres on each node represents the number of reads. Red spheres: un-annotated V, D, and J genes. The IgL read distributions are shown on a 2D-VJ-plot in which the length of each bar represents the relative number of reads. The x-axis represents 101 x IGLV&#954; and 3 x IGLV&#955; genes, and the y-axis represents 4 x IGLJ&#954; and 3 x IGLJ&#955; genes. The un-annotated V and J genes are represented on the right borderline. <a href="https://www.jove.com/files/ftp_upload/58804/58804fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58804/58804fig4.jpg" /> 
Figure 4: Global data visualization for human antibody repertoires. The overall repertoire profiles of each antibody class were visualized by 3D-VDJ-plot. The x-axis represents 56 IGHV genes ordered as on the chromosome. The y- and z-axis represents 27 IGHD and 6 IGHJ genes, respectively. The volume of spheres on each node represents the number of reads. Red spheres: un-annotated V, D, and J genes. The IgL reads are arrayed on the 2D-VJ-plot in which the length of each bar represents the relative number of the reads. The x-axis represents 41 x IGLV&#954; and 32 x IGLV&#955; genes, and the y-axis represents 5 x IGLJ&#954; and 5 x IGLJ&#955; genes. The un-annotated V- and J-genes are represented on the right borderline. <a href="https://www.jove.com/files/ftp_upload/58804/58804fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing at JoVE.com

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Here, we describe protocols for the analysis and visualization of the structure and constitution of whole antibody repertoires. This involves the acquisition of vast sequences of antibody RNA using next-generation sequencing.

The immense adaptability of antigen recognition by antibodies is the basis of the acquired immune system. Despite our understanding of the molecular ...

identification-mouse-human-antibody-repertoires-next-generation

Research

JoVE Journal

Immunology and Infection

12.3K Views.  University of Tsukuba. In deze video beschrijven we een eenvoudige en efficiënte methode om een antilichaamrepertoire op mondiaal niveau te visualiseren en te analyseren. Het is mogelijk om hele B-celrepertoires bij individuen en hun dynamische veranderingen te beschrijven in reactie op immuunstimulatie. Deze methode kan worden toegepast om pathologische monsters van patiënten van opkomende virale infecties te analyseren en om de gegevens te verkrijgen met ongekend inzicht in de pathogenese van de infectie.

Video: Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Single cell mRNA sequencing has made significant progress in the last several years and has become an important tool in the field of developmental biology. It has been successfully used to identify rare cell populations, discover novel marker genes, and decode spatial and temporal developmental information. The single cell method has also evolved from the microfluidic based Fluidigm C1 technology to the droplet-based solutions in the last two to three years. Here we used the heart as an example to demonstrate how to profile the mouse embryonic tissue cells using the droplet based scRNA-Seq method. In addition, we have integrated two strategies into the workflow to profile multiple samples in a single experiment. Using one of the integrated methods, we have simultaneously profiled more than 9,000 cells from eight heart samples. These methods will be valuable to the developmental biology field by providing a cost-effective way to simultaneously profile single cells from different genetic backgrounds, developmental stages, or anatomical locations.

Here we presented a multiplexed single cell mRNA sequencing method to profile gene expression in mouse embryonic tissues. The droplet-based single cell mRNA sequencing (scRNA-Seq) method in combination with multiplexing strategies can profile single cells from multiple samples simultaneously, which significantly reduces reagent costs and minimizes experimental batch effects.

Single cell mRNA sequencing has made significant progress in the last several years and has become an important tool in the field of developmental ...

Developmental Biology

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Immunopeptidomics is an emerging field that fuels and guides the development of vaccines and immunotherapies. More specifically, it refers to the science of investigating the composition of peptides presented by major histocompatibility complex (MHC) class I and class II molecules using mass spectrometry (MS) technology platforms. Among all the steps in an MS-based immunopeptidomics workflow, sample preparation is critically important for capturing high-quality data of therapeutic relevance. Here, step-by-step instructions are described to isolate MHC class I and II-associated peptides by immunoaffinity purification from quality control samples, from mouse (EL4 and A20), and human (JY) cell lines more specifically. The various reagents and specific antibodies are thoroughly described to isolate MHC-associated peptides from these cell lines, including the steps to verify the beads-binding efficiency of the antibody and the elution efficiency of the MHC-peptide complexes from the beads. The protocol can be used to establish and standardize an immunopeptidomics workflow, as well as to benchmark new protocols. Moreover, the protocol represents a great starting point for any non-experts in addition to foster the intra- and inter-laboratory reproducibility of the sample preparation procedure in immunopeptidomics.

Here, we present a protocol for the purification of MHC class I and class II peptide complexes from mouse and human cell lines providing high-quality immunopeptidomics data. The protocol focuses on sample preparation using commercially available antibodies.

Immunopeptidomics is an emerging field that fuels and guides the development of vaccines and immunotherapies. More specifically, it refers to the science ...

Biochemistry

Genome-wide Analysis of HDAC Inhibitor-mediated Modulation of microRNAs and mRNAs in B Cells Induced to Undergo Class-switch DNA Recombination and Plasma Cell Differentiation

Antibody responses are accomplished through several critical B cell-intrinsic processes, including somatic hypermutation (SHM), class-switch DNA recombination (CSR), and plasma cell differentiation. In recent years, epigenetic modifications or factors, such as histone deacetylation and microRNAs (miRNAs), have been shown to interact with B-cell genetic programs to shape antibody responses, while the dysfunction of epigenetic factors has been found to lead to autoantibody responses. Analyzing genome-wide miRNA and mRNA expression in B cells in response to epigenetic modulators is important for understanding the epigenetic regulation of B-cell function and antibody response. Here, we demonstrate a protocol for inducing B cells to undergo CSR and plasma cell differentiation, treating these B cells with histone deacetylase (HDAC) inhibitors (HDIs), and analyzing mRNA and microRNA expression. In this protocol, we directly analyze complementary DNA (cDNA) sequences using next-generation mRNA sequencing (mRNA-seq) and miRNA-seq technologies, mapping of the sequencing reads to the genome, and quantitative reverse transcription (qRT)-PCR. With these approaches, we have defined that, in B cells induced to undergo CSR and plasma cell differentiation, HDI, an epigenetic regulator, selectively modulates miRNA and mRNA expression and alters CSR and plasma cell differentiation.

Epigenetic factors can interact with genetic programs to modulate gene expression and regulate B cell function. By combining in vitro B-cell stimulation, qRT-PCR, and high-throughput microRNA-sequence and mRNA-sequence approaches, we can analyze the epigenetic modulation of miRNA and gene expression in B cells.

Antibody responses are accomplished through several critical B cell-intrinsic processes, including somatic hypermutation (SHM), class-switch DNA ...

Generation of Discriminative Human Monoclonal Antibodies from Rare Antigen-specific B Cells Circulating in Blood

Monoclonal antibodies (mAbs) are powerful tools useful for both fundamental research and in biomedicine. Their high specificity is indispensable when the antibody needs to distinguish between highly related structures (e.g., a normal protein and a mutated version thereof). The current way of generating such discriminative mAbs involves extensive screening of multiple Ab-producing B cells, which is both costly and time consuming. We propose here a rapid and cost-effective method for the generation of discriminative, fully human mAbs starting from human blood circulating B lymphocytes. The originality of this strategy is due to the selection of specific antigen binding B cells combined with the counter-selection of all other cells, using readily available Peripheral Blood Mononuclear Cells (PBMC). Once specific B cells are isolated, cDNA (complementary deoxyribonucleic acid) sequences coding for the corresponding mAb are obtained using single cell Reverse Transcription-Polymerase Chain Reaction (RT-PCR) technology and subsequently expressed in human cells. Within as little as 1 month, it is possible to produce milligrams of highly discriminative human mAbs directed against virtually any desired antigen naturally detected by the B cell repertoire.

We describe a method for the production of human antibodies specific for an antigen of interest, starting from rare B cells circulating in human blood. Generation of these natural antibodies is efficient and rapid, and the antibodies obtained can discriminate between highly related antigens.

Monoclonal antibodies (mAbs) are powerful tools useful for both fundamental research and in biomedicine. Their high specificity is indispensable when the ...

Characterization of Thymus-dependent and Thymus-independent Immunoglobulin Isotype Responses in Mice Using Enzyme-linked Immunosorbent Assay

Antibodies, also termed as immunoglobulins (Ig), secreted by differentiated B lymphocytes, plasmablasts/plasma cells, in humoral immunity provide a formidable defense against invading pathogens via diverse mechanisms. One major goal of vaccination is to induce protective antigen-specific antibodies to prevent life-threatening infections. Both thymus-dependent (TD) and thymus-independent (TI) antigens can elicit robust antigen-specific IgM responses and can also induce the production of isotype-switched antibodies (IgG, IgA and IgE) as well as the generation of memory B cells with the help provided by antigen presenting cells (APCs). Here, we describe a protocol to characterize TD and TI Ig isotype responses in mice using enzyme-linked immunosorbent assay (ELISA). In this protocol, TD and TI Ig responses are elicited in mice by intraperitoneal (i.p.) immunization with hapten-conjugated model antigens TNP-KLH (in alum) and TNP-polysaccharide (in PBS), respectively. To induce TD memory response, a booster immunization of TNP-KLH in alum is given at 3 weeks after the first immunization with the same antigen/adjuvant. Mouse sera are harvested at different time points before and after immunization. Total serum Ig levels and TNP-specific antibodies are subsequently quantified using Ig isotype-specific Sandwich and indirect ELISA, respectively. In order to correctly quantify the serum concentration of each Ig isotype, the samples need to be appropriately diluted to fit within the linear range of the standard curves. Using this protocol, we have consistently obtained reliable results with high specificity and sensitivity. When used in combination with other complementary methods such as flow cytometry, in vitro culture of splenic B cells and immunohistochemical staining (IHC), this protocol will allow researchers to gain a comprehensive understanding of antibody responses in a given experimental setting.

In this paper, we describe a protocol to characterize T-dependent and T-independent immunoglobulin (Ig) isotype responses in mice using ELISA. This method used alone or in combination with flow cytometry will allow researchers to identify differences in B cell-mediated Ig isotype responses in mice following T-dependent and T-independent antigen immunization.

Antibodies, also termed as immunoglobulins (Ig), secreted by differentiated B lymphocytes, plasmablasts/plasma cells, in humoral immunity provide a ...

Chemically-blocked Antibody Microarray for Multiplexed High-throughput Profiling of Specific Protein Glycosylation in Complex Samples

In this study, we describe an effective protocol for use in a multiplexed high-throughput antibody microarray with glycan binding protein detection that allows for the glycosylation profiling of specific proteins. Glycosylation of proteins is the most prevalent post-translational modification found on proteins, and leads diversified modifications of the physical, chemical, and biological properties of proteins. Because the glycosylation machinery is particularly susceptible to disease progression and malignant transformation, aberrant glycosylation has been recognized as early detection biomarkers for cancer and other diseases. However, current methods to study protein glycosylation typically are too complicated or expensive for use in most normal laboratory or clinical settings and a more practical method to study protein glycosylation is needed. The new protocol described in this study makes use of a chemically blocked antibody microarray with glycan-binding protein (GBP) detection and significantly reduces the time, cost, and lab equipment requirements needed to study protein glycosylation. In this method, multiple immobilized glycoprotein-specific antibodies are printed directly onto the microarray slides and the N-glycans on the antibodies are blocked. The blocked, immobilized glycoprotein-specific antibodies are able to capture and isolate glycoproteins from a complex sample that is applied directly onto the microarray slides. Glycan detection then can be performed by the application of biotinylated lectins and other GBPs to the microarray slide, while binding levels can be determined using Dylight 549-Streptavidin. Through the use of an antibody panel and probing with multiple biotinylated lectins, this method allows for an effective glycosylation profile of the different proteins found in a given human or animal sample to be developed.
Introduction
Glycosylation of protein, which is the most ubiquitous post-translational modification on proteins, modifies the physical, chemical, and biological properties of a protein, and plays a fundamental role in various biological processes1-6. Because the glycosylation machinery is particularly susceptible to disease progression and malignant transformation, aberrant glycosylation has been recognized as early detection biomarkers for cancer and other diseases 7-12. In fact, most current cancer biomarkers, such as the L3 fraction of &alpha;-1 fetoprotein (AFP) for hepatocellular carcinoma 13-15, and CA199 for pancreatic cancer 16, 17 are all aberrant glycan moieties on glycoproteins. However, methods to study protein glycosylation have been complicated, and not suitable for routine laboratory and clinical settings. Chen et al. has recently invented a chemically blocked antibody microarray with a glycan-binding protein (GBP) detection method for high-throughput and multiplexed profile glycosylation of native glycoproteins in a complex sample 18. In this affinity based microarray method, multiple immobilized glycoprotein-specific antibodies capture and isolate glycoproteins from the complex mixture directly on the microarray slide, and the glycans on each individual captured protein are measured by GBPs. Because all normal antibodies contain N-glycans which could be recognized by most GBPs, the critical step of this method is to chemically block the glycans on the antibodies from binding to GBP. In the procedure, the cis-diol groups of the glycans on the antibodies were first oxidized to aldehyde groups by using NaIO4 in sodium acetate buffer avoiding light. The aldehyde groups were then conjugated to the hydrazide group of a cross-linker, 4-(4-N-MaleimidoPhenyl)butyric acid Hydrazide HCl (MPBH), followed by the conjugation of a dipeptide, Cys-Gly, to the maleimide group of the MPBH. Thus, the cis-diol groups on glycans of antibodies were converted into bulky none hydroxyl groups, which hindered the lectins and other GBPs bindings to the capture antibodies. This blocking procedure makes the GBPs and lectins bind only to the glycans of captured proteins. After this chemically blocking, serum samples were incubated with the antibody microarray, followed by the glycans detection by using different biotinylated lectins and GBPs, and visualized with Cy3-streptavidin. The parallel use of an antibody panel and multiple lectin probing provides discrete glycosylation profiles of multiple proteins in a given sample 18-20. This method has been used successfully in multiple different labs 1, 7, 13, 19-31. However, stability of MPBH and Cys-Gly, complicated and extended procedure in this method affect the reproducibility, effectiveness and efficiency of the method. In this new protocol, we replaced both MPBH and Cys-Gly with one much more stable reagent glutamic acid hydrazide (Glu-hydrazide), which significantly improved the reproducibility of the method, simplified and shorten the whole procedure so that the it can be completed within one working day. In this new protocol, we describe the detailed procedure of the protocol which can be readily adopted by normal labs for routine protein glycosylation study and techniques which are necessary to obtain reproducible and repeatable results.

In this study, we describe an improved protocol for a multiplexed high-throughput antibody microarray with lectin detection method that can be used in glycosylation profiling of specific proteins. This protocol features new reliable reagents and significantly reduces the time, cost, and lab equipment requirements as compared to the previous procedure.

 In this study, we describe an effective protocol for use in a multiplexed high-throughput antibody microarray with glycan binding protein detection that ...

Biology

Single-cell Screening Method for the Selection and Recovery of Antibodies with Desired Specificities from Enriched Human Memory B Cell Populations

The human antibody repertoire represents a largely untapped source of potential therapeutic antibodies and useful biomarkers. While current computational methods, such as next generation sequencing (NGS), yield enormous sets of data on the antibody repertoire at the sequence level, functional data is required to identify which sequences are relevant for a particular antigen or set of antigens. Here, we describe a method to identify and recover individual antigen-specific antibodies from peripheral blood mononuclear cells (PBMCs) from a human blood donor. This method utilizes an initial enrichment of mature B cells and requires a combination of phenotypic cell markers and fluorescently-labeled protein to isolate IgG memory B cells via flow cytometry. The heavy and light chain variable regions are then cloned and re-screened. Although limited to the memory B cell compartment, this method takes advantage of flow cytometry to interrogate millions of B cells and returns paired heavy and light chain sequences from a single cell in a format ready for expression and confirmation of specificity. Antibodies recovered with this method can be considered for therapeutic potential, but can also link specificity and function with bioinformatic approaches to assess the B cell repertoire within individuals.

The BSelex method to identify and recover individual antigen-specific antibodies from human peripheral blood mononuclear cells combines flow cytometry with single cell PCR and cloning.

The human antibody repertoire represents a largely untapped source of potential therapeutic antibodies and useful biomarkers. While current computational ...

T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing

Immunological memory, the hallmark of adaptive immunity, is orchestrated by T and B lymphocytes. In circulation and different organs, there are billions of unique T and B cell clones, and each one can bind a specific antigen, leading to proliferation, differentiation and/or cytokine secretion. The vast heterogeneity in T and B cells is generated by random recombination of different genetic segments. Next-generation sequencing (NGS) technologies, developed in the last decade, enable an unprecedented in-depth view of the T and B cell receptor immune repertoire. Studies in various inflammatory conditions, immunodeficiencies, infections and malignancies demonstrated marked changes in clonality, gene usage, and biophysical properties of immune repertoire, providing important insights about the role of adaptive immune responses in different disorders.
Here, we provide a detailed protocol for NGS of immune repertoire of T and B cells from blood and tissue. We present a pipeline starting from DNA isolation through library preparation, sequencing on NGS sequencer and ending with basic analyses. This method enables exploration of specific T and B cells at the nucleotide or amino-acid level, and thus can identify dynamic changes in lymphocyte populations and diversity parameters in different diseases. This technique is slowly entering clinical practice and has the potential for identification of novel biomarkers, risk stratification and precision medicine.

The current protocol describes a method for DNA isolation from blood samples and intestinal biopsies, generation of TCR&#946; and IGH PCR libraries for next-generation sequencing, performance of a NGS run and basic data analysis.

Immunological memory, the hallmark of adaptive immunity, is orchestrated by T and B lymphocytes. In circulation and different organs, there are billions ...

Detection of SARS-CoV-2 Receptor-Binding Domain Antibody using a HiBiT-Based Bioreporter

The emergence of the COVID-19 pandemic has increased the need for better serological detection methods to determine the epidemiologic impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The increasing number of SARS-CoV-2 infections raises the need for better antibody detection assays. Current antibody detection methods compromise sensitivity for speed or are sensitive but time-consuming. A large proportion of SARS-CoV-2-neutralizing antibodies target the receptor-binding domain (RBD), one of the primary immunogenic compartments of SARS-CoV-2. We have recently designed and developed a highly sensitive, bioluminescent-tagged RBD (NanoLuc HiBiT-RBD) to detect SARS-CoV-2 antibodies. The following text describes the procedure to produce the HiBiT-RBD complex and a fast assay to evaluate the presence of RBD-targeting antibodies using this tool. Due to the durability of the HiBiT-RBD protein product over a wide range of temperatures and the shorter experimental procedure that can be completed within 1 h, the protocol can be considered as a more efficient alternative to detect SARS-CoV-2 antibodies in patient serum samples.

The outlined protocol describes the procedure for producing the HiBiT-receptor-binding domain protein complex and its application for fast and sensitive detection of SARS-CoV-2 antibodies.

The emergence of the COVID-19 pandemic has increased the need for better serological detection methods to determine the epidemiologic impact of severe ...

Interactome-Seq: A Protocol for Domainome Library Construction, Validation and Selection by Phage Display and Next Generation Sequencing

Folding reporters are proteins with easily identifiable phenotypes, such as antibiotic resistance, whose folding and function is compromised when fused to poorly folding proteins or random open reading frames. We have developed a strategy where, by using TEM-1 &#946;-lactamase (the enzyme conferring ampicillin resistance) on a genomic scale, we can select collections of correctly folded protein domains from the coding portion of the DNA of any intronless genome. The protein fragments obtained by this approach, the so called &#34;domainome&#34;, will be well expressed and soluble, making them suitable for structural/functional studies.
By cloning and displaying the &#34;domainome&#34; directly in a phage display system, we have showed that it is possible to select specific protein domains with the desired binding properties (e.g., to other proteins or to antibodies), thus providing essential experimental information for gene annotation or antigen identification.
The identification of the most enriched clones in a selected polyclonal population can be achieved by using novel next-generation sequencing technologies (NGS). For these reasons, we introduce deep sequencing analysis of the library itself and the selection outputs to provide complete information on diversity, abundance and precise mapping of each of the selected fragment. The protocols presented here show the key steps for library construction, characterization, and validation.

The protocols described allow the construction, characterization and selection (against the target of choice) of a &#34;domainome&#34; library made from any DNA source. This is achieved by a research pipeline that combines different technologies: phage display, a folding reporter and next generation sequencing with a web tool for data analysis.

Folding reporters are proteins with easily identifiable phenotypes, such as antibiotic resistance, whose folding and function is compromised when fused to ...

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

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